diff --git a/.codecov.yml b/.codecov.yml index fe57f113..a92352ef 100644 --- a/.codecov.yml +++ b/.codecov.yml @@ -25,9 +25,9 @@ coverage: # This encourages small PR's as they are easier to test. patch: default: - target: 90% + target: 10% if_not_found: failure - if_ci_failed: error + if_ci_failed: failure # We upload additional information on branching with pytest-cov `--cov-branch` # This information can be used by codecov.com to increase analysis of code diff --git a/.flake8 b/.flake8 index 0bb5e37d..820d1af1 100644 --- a/.flake8 +++ b/.flake8 @@ -10,3 +10,6 @@ extend-ignore = E203 # No lambdas — too strict E731 + E722 + F405 + F403 diff --git a/.github/workflows/dist.yaml b/.github/workflows/dist.yaml index ea55698e..535d2a39 100644 --- a/.github/workflows/dist.yaml +++ b/.github/workflows/dist.yaml @@ -10,14 +10,20 @@ on: - main - development - # When a push occurs on a PR that targets these branches + # Trigger on open/push to a PR targeting one of these branches pull_request: + types: + - opened + - synchronize + - reopened + - ready_for_review branches: - main - development jobs: dist: + if: ${{ !github.event.pull_request.draft }} runs-on: ubuntu-latest steps: @@ -38,7 +44,7 @@ jobs: - name: Twine check run: | pip install twine - last_dist=$(ls -t dist/ContextuaRL-*.tar.gz | head -n 1) + last_dist=$(ls -t dist/carl-*.tar.gz | head -n 1) twine_output=`twine check "$last_dist"` if [[ "$twine_output" != "Checking $last_dist: PASSED" ]] then @@ -49,7 +55,7 @@ jobs: - name: Install dist run: | - last_dist=$(ls -t dist/ContextuaRL-*.tar.gz | head -n 1) + last_dist=$(ls -t dist/carl-*.tar.gz | head -n 1) pip install $last_dist - name: PEP 561 Compliance diff --git a/.github/workflows/docs.yaml b/.github/workflows/docs.yaml new file mode 100644 index 00000000..527ae8ff --- /dev/null +++ b/.github/workflows/docs.yaml @@ -0,0 +1,64 @@ +name: docs + +on: + # Manual trigger option in github + # This won't push to github pages where docs are hosted due + # to the gaurded if statement in those steps + workflow_dispatch: + + # Trigger on push to these branches + push: + branches: + - main + + # Trigger on a open/push to a PR targeting one of these branches + pull_request: + branches: + - main + +env: + name: CARL + +jobs: + build-and-deploy: + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v3 + + - name: Setup Python + uses: actions/setup-python@v4 + with: + python-version: "3.9" + + - name: Install dependencies + run: | + pip install ".[docs]" + - name: Make docs + run: | + make clean + make doc + - name: Pull latest gh_pages + if: (contains(github.ref, 'version_0.2.0') || contains(github.ref, 'main')) && github.event_name == 'push' + run: | + cd .. + git clone https://github.com/${{ github.repository }}.git --branch gh_pages --single-branch gh_pages + - name: Copy new docs into gh_pages + if: (contains(github.ref, 'version_0.2.0') || contains(github.ref, 'main')) && github.event_name == 'push' + run: | + branch_name=${GITHUB_REF##*/} + cd ../gh_pages + rm -rf $branch_name + cp -r ../${{ env.name }}/docs/build/html $branch_name + - name: Push to gh_pages + if: (contains(github.ref, 'version_0.2.0') || contains(github.ref, 'main')) && github.event_name == 'push' + run: | + last_commit=$(git log --pretty=format:"%an: %s") + cd ../gh_pages + branch_name=${GITHUB_REF##*/} + git add $branch_name/ + git config --global user.name 'Github Actions' + git config --global user.email 'not@mail.com' + git remote set-url origin https://x-access-token:${{ secrets.GITHUB_TOKEN }}@github.com/${{ github.repository }} + git commit -am "$last_commit" + git push diff --git a/.github/workflows/precommit.yaml b/.github/workflows/precommit.yaml index d6ccd7d7..97eaacb3 100644 --- a/.github/workflows/precommit.yaml +++ b/.github/workflows/precommit.yaml @@ -12,12 +12,18 @@ on: # When a push occurs on a PR that targets these branches pull_request: + types: + - opened + - synchronize + - reopened + - ready_for_review branches: - main - development jobs: run-all-files: + if : ${{ !github.event.pull_request.draft }} runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 diff --git a/.github/workflows/tests.yaml b/.github/workflows/tests.yaml index c9ac8832..fe1a8bb7 100644 --- a/.github/workflows/tests.yaml +++ b/.github/workflows/tests.yaml @@ -12,6 +12,11 @@ on: # When a push occurs on a PR that targets these branches pull_request: + types: + - opened + - synchronize + - reopened + - ready_for_review branches: - main - development @@ -36,6 +41,7 @@ jobs: ubuntu: name: ${{ matrix.os }}-${{ matrix.python-version }}-${{ matrix.kind }} + if: ${{ !github.event.pull_request.draft }} runs-on: ${{ matrix.os }} strategy: @@ -98,12 +104,13 @@ jobs: run: | python -m pip install --upgrade pip python setup.py sdist - last_dist=$(ls -t dist/ContextuaRL-*.tar.gz | head -n 1) + last_dist=$(ls -t dist/carl-*.tar.gz | head -n 1) pip install $last_dist[dev,dm_control] - name: Tests timeout-minutes: 60 run: | + echo "Running all tests..." if [[ ${{ matrix.kind }} == 'conda' ]]; then PYTHON=$CONDA/envs/testenv/bin/python3 export PATH="$CONDA/envs/testenv/bin:$PATH" diff --git a/.gitignore b/.gitignore index ce1a3128..45776fd5 100644 --- a/.gitignore +++ b/.gitignore @@ -16,13 +16,23 @@ CARL.egg-info carl.egg-info .mypy_cache .pytest_cache -.coverage +.coverage* exp_sweep multirun outputs -experiments +testvenv +*.egg-info runs +*.png +*.pdf +*.csv +*.pickle +*.ipynb_checkpoints +*optgap* +*smac3* +*.json generated -*egg* core -*.png \ No newline at end of file +*.tex +build +target \ No newline at end of file diff --git a/.gitmodules b/.gitmodules index 091048df..ba771c4d 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,9 +1,6 @@ -[submodule "src/envs/rna/learna"] - path = src/envs/rna/learna - url = https://github.com/automl/learna.git -[submodule "src/envs/mario/TOAD-GUI"] - path = src/envs/mario/TOAD-GUI +[submodule "carl/envs/mario/TOAD-GUI"] + path = carl/envs/mario/TOAD-GUI url = https://github.com/Mawiszus/TOAD-GUI -[submodule "src/envs/mario/Mario-AI-Framework"] - path = src/envs/mario/Mario-AI-Framework +[submodule "carl/envs/mario/Mario-AI-Framework"] + path = carl/envs/mario/Mario-AI-Framework url = https://github.com/frederikschubert/Mario-AI-Framework \ No newline at end of file diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index f4862637..1d0bfffe 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -43,13 +43,6 @@ repos: always_run: false additional_dependencies: ["toml"] # Needed to parse pyproject.toml - - repo: https://github.com/pre-commit/mirrors-mypy - rev: v0.930 - hooks: - - id: mypy - name: mypy carl - files: carl/.* - - repo: https://github.com/pycqa/flake8 rev: 6.0.0 hooks: diff --git a/Makefile b/Makefile index a1c05b79..995451d5 100644 --- a/Makefile +++ b/Makefile @@ -24,7 +24,7 @@ PYTEST ?= python -m pytest CTAGS ?= ctags PIP ?= python -m pip MAKE ?= make -BLACK ?= black +BLACK ?= python -m black ISORT ?= isort PYDOCSTYLE ?= pydocstyle MYPY ?= mypy @@ -88,7 +88,7 @@ build: $(PYTHON) setup.py sdist doc: - $(MAKE) -C ${DOCDIR} all + $(MAKE) -C ${DOCDIR} docs @echo @echo "View docs at:" @echo ${INDEX_HTML} diff --git a/README.md b/README.md index 10df90d5..6aecd0fb 100644 --- a/README.md +++ b/README.md @@ -41,7 +41,7 @@ pip install . This will only install the basic classic control environments, which should run on most operating systems. For the full set of environments, use the install options: ```bash -pip install -e .[box2d, brax, mario, dm_control] +pip install -e .[box2d,brax,dm_control,mario,rna] ``` These may not be compatible with Windows systems. Box2D environment may need to be installed via conda on MacOS systems: @@ -50,13 +50,26 @@ conda install -c conda-forge gym-box2d ``` In general, we test on Linux systems, but aim to keep the benchmark compatible with MacOS as much as possible. -Mario at this point, however, will not run on any operation system besides Linux +RNA and Mario at this point, however, will not run on any operation system besides Linux. -To install the additional requirements for ToadGAN: +To install ToadGAN for the Mario environment: ```bash -javac carl/envs/mario/Mario-AI-Framework/**/*.java +git submodule update --init --recursive + +# if this does not work, clone manually +git clone https://github.com/frederikschubert/Mario-AI-Framework carl/envs/mario/Mario-AI-Framework +git clone https://github.com/Mawiszus/TOAD-GUI carl/envs/mario/TOAD-GUI + +# System requirements +sudo apt install libfreetype6-dev xvfb + +# Compile java source files +cd carl/envs/mario/Mario-AI-Framework/src +javac *.java ``` +If you want to use RNA, please take a look at the associated [ReadME](carl/envs/rna/readme.md). + ## CARL's Contextual Extension CARL contextually extends the environment by making the context visible and configurable. During training we therefore can encounter different contexts and train for generalization. @@ -68,16 +81,22 @@ Different instiations can be achieved by setting the context features to differe ## Cite Us If you use CARL in your research, please cite our paper on the benchmark: ```bibtex -@inproceedings{BenEim2021a, - title = {CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning}, - author = {Carolin Benjamins and Theresa Eimer and Frederik Schubert and André Biedenkapp and Bodo Rosenhahn and Frank Hutter and Marius Lindauer}, - booktitle = {NeurIPS 2021 Workshop on Ecological Theory of Reinforcement Learning}, - year = {2021}, - month = dec +@inproceedings { BenEim2023a, + author = {Carolin Benjamins and + Theresa Eimer and + Frederik Schubert and + Aditya Mohan and + Sebastian Döhler and + André Biedenkapp and + Bodo Rosenhahn and + Frank Hutter and + Marius Lindauer}, + title = {Contextualize Me - The Case for Context in Reinforcement Learning}, + journal = {Transactions on Machine Learning Research}, + year = {2023}, } -``` -You can find the code and experiments for this paper in the `neurips_ecorl_workshop_2021` branch. +``` ## References [OpenAI gym, Brockman et al., 2016. arXiv preprint arXiv:1606.01540](https://arxiv.org/pdf/1606.01540.pdf) diff --git a/carl/__init__.py b/carl/__init__.py index 419f5f2b..23a39d48 100644 --- a/carl/__init__.py +++ b/carl/__init__.py @@ -1,19 +1,19 @@ __license__ = "Apache-2.0 License" -__version__ = "0.2.0" +__version__ = "1.0.0" __author__ = "Carolin Benjamins, Theresa Eimer, Frederik Schubert, André Biedenkapp, Aditya Mohan, Sebastian Döhler" import datetime name = "CARL" -package_name = "ContextuaRL" +package_name = "carl" author = __author__ author_email = "benjamins@tnt.uni-hannover.de" description = "CARL- Contextually Adaptive Reinforcement Learning" url = "https://www.automl.org/" project_urls = { - "Documentation": "https://carl.readthedocs.io/en/latest/", + "Documentation": "https://automl.github.io/CARL", "Source Code": "https://github.com/https://github.com/automl/CARL", } copyright = f""" diff --git a/carl/context/augmentation.py b/carl/context/augmentation.py deleted file mode 100644 index 3ecaba3f..00000000 --- a/carl/context/augmentation.py +++ /dev/null @@ -1,40 +0,0 @@ -from typing import Any, List, Union - -import numpy as np - - -def add_gaussian_noise( - default_value: Union[float, List[float]], - percentage_std: Union[float, Any] = 0.01, - random_generator: np.random.Generator = None, -) -> Union[float, Any]: - """ - Add gaussian noise to default value. - - Parameters - ---------- - default_value: Union[float, List[float]] - Mean of normal distribution. Can be a scalar or a list of floats. If it is a list(-like) with length n, the - output will also be of length n. - percentage_std: float, optional = 0.01 - Relative standard deviation, multiplied with default value (mean) is standard deviation of normal distribution. - If the default value is 0, percentage_std is assumed to be the absolute standard deviation. - random_generator: np.random.Generator, optional = None - Optional random generator to ensure deterministic behavior. - - Returns - ------- - Union[float, List[float]] - Default value with gaussian noise. If input was list (or array) with length n, output is also list (or array) - with length n. - """ - if type(default_value) in [int, float] and default_value != 0: - std = percentage_std * np.abs(default_value) - else: - std = percentage_std - mean = np.zeros_like(default_value) - if not random_generator: - random_generator = np.random.default_rng() - value = default_value + random_generator.normal(loc=mean, scale=std) - - return value diff --git a/carl/context/context_space.py b/carl/context/context_space.py new file mode 100644 index 00000000..3deb272b --- /dev/null +++ b/carl/context/context_space.py @@ -0,0 +1,225 @@ +from __future__ import annotations + +from typing import List + +import gymnasium.spaces as spaces +import numpy as np +from ConfigSpace.hyperparameters import ( + CategoricalHyperparameter, + Hyperparameter, + NormalFloatHyperparameter, + NumericalHyperparameter, + UniformFloatHyperparameter, + UniformIntegerHyperparameter, +) +from typing_extensions import TypeAlias + +from carl.utils.types import Context, Contexts + +ContextFeature: TypeAlias = Hyperparameter +NumericalContextFeature: TypeAlias = NumericalHyperparameter +NormalFloatContextFeature: TypeAlias = NormalFloatHyperparameter +UniformFloatContextFeature: TypeAlias = UniformFloatHyperparameter +UniformIntegerContextFeature: TypeAlias = UniformIntegerHyperparameter +CategoricalContextFeature: TypeAlias = CategoricalHyperparameter + + +class ContextSpace(object): + def __init__(self, context_space: dict[str, ContextFeature]) -> None: + """Context space + + Parameters + ---------- + context_space : dict[str, ContextFeature] + Raw definition of the context space. + """ + self.context_space = context_space + + @property + def context_feature_names(self) -> list[str]: + """ + Context feature names. + + Returns + ------- + list[str] + Context features names. + """ + return list(self.context_space.keys()) + + def insert_defaults( + self, context: Context, context_keys: List[str] | None = None + ) -> Context: + """Insert default context if keys missing. + + Parameters + ---------- + context : Context + The context. + context_keys : List[str] | None, optional + Insert defaults only for certain keys, by default None. + + Returns + ------- + Context + The filled context with default values. + """ + context_with_defaults = self.get_default_context() + + # insert defaults only for certain keys + if context_keys: + context_with_defaults = { + key: context_with_defaults[key] for key in context_keys + } + + context_with_defaults.update(context) + return context_with_defaults + + def verify_context(self, context: Context) -> bool: + """Verify context. + + Check if context feature names are correct and the + values are in bounds. + + Parameters + ---------- + context : Context + The context to check. + + Returns + ------- + bool + True if valid, False if not. + """ + is_valid = True + cfs = self.context_feature_names + for cfname, v in context.items(): + # Check if context feature exists in space + # by checking name + if cfname not in cfs: + is_valid = False + break + + # Check if context feature value is in bounds + cf = self.context_space[cfname] + if isinstance(cf, NumericalContextFeature): + if not (cf.lower <= v <= cf.upper): + is_valid = False + break + return is_valid + + def get_default_context(self) -> Context: + """Get the default context from the context space. + + Returns + ------- + Context + Default context. + """ + context = {cf.name: cf.default_value for cf in self.context_space.values()} + return context + + def get_lower_and_upper_bound( + self, context_feature_name: str + ) -> tuple[float, float]: + """Get lower and upper bounds for each context feature from the context space. + + Parameters + ---------- + context_feature_name : str + Name of context feature to get the bounds for. + + Returns + ------- + tuple[float, float] + Lower and upper bound as a tuple. + """ + cf = self.context_space[context_feature_name] + bounds = (cf.lower, cf.upper) + return bounds + + def to_gymnasium_space( + self, context_feature_names: List[str] | None = None, as_dict: bool = False + ) -> spaces.Space: + """Convert the context space to a gymnasium space (box). + + Parameters + ---------- + context_feature_names : List[str] | None, optional + The context features that should be included in the space, by default None. + If it is None, then use all available context features. + as_dict : bool, optional + If True, create a dict gymnasium space, by default False. If False, + context feature values as a vector. + + Returns + ------- + spaces.Space + Gymnasium space which can be used as an observation space. + """ + if context_feature_names is None: + context_feature_names = self.context_feature_names + if as_dict: + context_space = {} + + for cf_name in context_feature_names: + context_feature = self.context_space[cf_name] + if isinstance(context_feature, NumericalContextFeature): + context_space[context_feature.name] = spaces.Box( + low=context_feature.lower, high=context_feature.upper + ) + else: + context_space[context_feature.name] = spaces.Discrete( + len(context_feature.choices) + ) + return spaces.Dict(context_space) + else: + low = np.array( + [self.context_space[cf].lower for cf in context_feature_names] + ) + high = np.array( + [self.context_space[cf].upper for cf in context_feature_names] + ) + + return spaces.Box(low=low, high=high, dtype=np.float32) + + def sample_contexts( + self, context_keys: List[str] | None = None, size: int = 1 + ) -> Context | List[Contexts]: + """Sample a number of contexts from the space. + + Parameters + ---------- + context_keys : List[str] | None, optional + The context feature names to sample for, by default None + size : int, optional + The number of contexts to sample, by default 1 + + Returns + ------- + Context | List[Contexts] + A context or list of contexts. Always filled with defaults + if context features missing. + + Raises + ------ + ValueError + When elements of context_keys are not valid. + """ + if context_keys is None: + context_keys = self.context_space.keys() + else: + for key in context_keys: + if key not in self.context_space.keys(): + raise ValueError(f"Invalid context feature name: {key}") + + contexts = [] + for _ in range(size): + context = {cf.name: cf.sample() for cf in self.context_space.values()} + context = self.insert_defaults(context, context_keys) + contexts += [context] + + if size == 1: + return contexts[0] + else: + return contexts diff --git a/carl/context/sampler.py b/carl/context/sampler.py new file mode 100644 index 00000000..daeafe1a --- /dev/null +++ b/carl/context/sampler.py @@ -0,0 +1,62 @@ +from __future__ import annotations + +from ConfigSpace import ConfigurationSpace +from omegaconf import DictConfig + +from carl.context.context_space import ContextFeature, ContextSpace +from carl.context.search_space_encoding import search_space_to_config_space +from carl.utils.types import Context, Contexts + + +class ContextSampler(ConfigurationSpace): + def __init__( + self, + context_distributions: list[ContextFeature] + | dict[str, ContextFeature] + | str + | DictConfig, + context_space: ContextSpace, + seed: int, + name: str | None = None, + ): + self.context_distributions = context_distributions + super().__init__(name=name, seed=seed) + + if isinstance(context_distributions, list): + self.add_context_features(context_distributions) + elif isinstance(context_distributions, dict): + self.add_context_features(context_distributions.values()) + elif type(context_distributions) in [str, DictConfig]: + cs = search_space_to_config_space(context_distributions) + self.add_context_features(cs.get_hyperparameters()) + else: + raise ValueError( + f"Unknown type `{type(context_distributions)}` for `context_distributions`." + ) + + self.context_feature_names = [cf.name for cf in self.get_context_features()] + self.context_space = context_space + + def add_context_features(self, context_features: list[ContextFeature]) -> None: + self.add_hyperparameters(context_features) + + def get_context_features(self) -> list[ContextFeature]: + return list(self.values()) + + def sample_contexts(self, n_contexts: int) -> Contexts: + contexts = self._sample_contexts(size=n_contexts) + + # Convert to dict + contexts = {i: C for i, C in enumerate(contexts)} + + return contexts + + def _sample_contexts(self, size: int = 1) -> list[Context]: + contexts = self.sample_configuration(size=size) + default_context = self.context_space.get_default_context() + + if size == 1: + contexts = [contexts] + contexts = [dict(default_context | dict(C)) for C in contexts] + + return contexts diff --git a/carl/context/sampling.py b/carl/context/sampling.py deleted file mode 100644 index 31fec750..00000000 --- a/carl/context/sampling.py +++ /dev/null @@ -1,185 +0,0 @@ -# flake8: noqa: W605 -from typing import Any, Dict, List, Tuple - -import numpy as np -from scipy.stats import norm - -from carl import envs -from carl.utils.types import Context, Contexts - - -def get_default_context_and_bounds( - env_name: str, -) -> Tuple[Dict[Any, Any], Dict[Any, Any]]: - """ - Get context feature defaults and bounds for environment. - - Parameters - ---------- - env_name: str - Name of CARLEnv. - - Returns - ------- - Tuple[Dict[Any, Any], Dict[Any, Any]] - Context feature defaults as dictionary, context feature bounds as dictionary. - Keys are the names of the context features. - - Context feature bounds can be in following formats: - int/float context features: - ``"MAIN_ENGINE_POWER": (0, 50, float)`` - - list of int/float context feature: - ``"target_structure_ids": (0, np.inf, [list, int])`` - - categorical context features: - ``"VEHICLE": (None, None, "categorical", np.arange(0, len(PARKING_GARAGE)))`` - """ - # TODO make less hacky / make explicit - env_defaults = getattr(envs, f"{env_name}_defaults") - env_bounds = getattr(envs, f"{env_name}_bounds") - - return env_defaults, env_bounds - - -def sample_contexts( - env_name: str, - context_feature_args: List[str], - num_contexts: int, - default_sample_std_percentage: float = 0.05, - fallback_sample_std: float = 0.1, -) -> Dict[int, Dict[str, Any]]: - """ - Sample contexts. - - Control which/how the context features are sampled with `context_feature_args`. - Categorical context features are sampled radonmly via the given choices in the context bounds. - For continuous context features a new value is sampled in the following way: - - .. math:: x_{cf,new} \sim \mathcal{N}(x_{cf, default}, \sigma_{rel} \cdot x_{cf, default}) - - :math:`x_{cf,new}`: New context feature value - - :math:`x_{cf, default}`: Default context feature value - - :math:`\sigma_{rel}`: Relative standard deviation, parametrized in `context_feature_args` - by providing e.g. `["_std", "0.05"]`. - - Examples - -------- - Sampling two contexts for the CARLAcrobotEnv and changing only the context feature link_length_2. - >>> sample_contexts("CARLAcrobotEnv", ["link_length_2"], 2) - {0: {'link_length_1': 1, - 'link_length_2': 1.0645201049835367, - 'link_mass_1': 1, - 'link_mass_2': 1, - 'link_com_1': 0.5, - 'link_com_2': 0.5, - 'link_moi': 1, - 'max_velocity_1': 12.566370614359172, - 'max_velocity_2': 28.274333882308138}, - 1: {'link_length_1': 1, - 'link_length_2': 1.011885635790618, - 'link_mass_1': 1, - 'link_mass_2': 1, - 'link_com_1': 0.5, - 'link_com_2': 0.5, - 'link_moi': 1, - 'max_velocity_1': 12.566370614359172, - 'max_velocity_2': 28.274333882308138}} - - - Parameters - ---------- - env_name: str - Name of MetaEnvironment - context_feature_args: List[str] - All arguments from the parser, e.g., ["context_feature_0", "context_feature_1", "context_feature_1_std", "0.05"] - num_contexts: int - Number of contexts to sample. - default_sample_std_percentage: float, optional - The default relative standard deviation to use if _std is not specified. The default is - 0.05. - fallback_sample_std: float, optional - The fallback relative standard deviation. Defaults to 0.1. - - Returns - ------- - Dict[int, Dict[str, Any]] - Dictionary containing the sampled contexts. Keys are integers, values are Dicts containing the context feature - names as keys and context feature values as values, e.g., - - """ - # Get default context features and bounds - env_defaults, env_bounds = get_default_context_and_bounds(env_name=env_name) - - # Create sample distributions/rules - sample_dists = {} - for context_feature_name in env_defaults.keys(): - if context_feature_name in context_feature_args: - if f"{context_feature_name}_mean" in context_feature_args: - sample_mean = float( - context_feature_args[ - context_feature_args.index(f"{context_feature_name}_mean") + 1 - ] - ) - else: - sample_mean = env_defaults[context_feature_name] - - if f"{context_feature_name}_std" in context_feature_args: - sample_std = float( - context_feature_args[ - context_feature_args.index(f"{context_feature_name}_std") + 1 - ] - ) - else: - sample_std = default_sample_std_percentage * np.abs(sample_mean) - - if sample_mean == 0: - # Fallback sample standard deviation. Necessary if the sample mean is 0. - # In this case the sample standard deviation would be 0 as well and we would always sample - # the sample mean. Therefore we use a fallback sample standard deviation. - sample_std = fallback_sample_std # TODO change this back to sample_std - - random_variable = norm(loc=sample_mean, scale=sample_std) - context_feature_type = env_bounds[context_feature_name][2] - sample_dists[context_feature_name] = (random_variable, context_feature_type) - - # Sample contexts - contexts: Contexts = {} - for i in range(0, num_contexts): - c: Context = {} - # k = name of context feature - for k in env_defaults.keys(): - if k in sample_dists.keys(): - # If we have a special sampling distribution/rule for context feature k - random_variable = sample_dists[k][0] - context_feature_type = sample_dists[k][1] - lower_bound, upper_bound = env_bounds[k][0], env_bounds[k][1] - if context_feature_type == list: - length = np.random.randint( - 500000 - ) # TODO should we allow lists to be this long? or should we parametrize this? - arg_class = sample_dists[k][1][1] - context_list = random_variable.rvs(size=length) - context_list = np.clip(context_list, lower_bound, upper_bound) - c[k] = [arg_class(c) for c in context_list] - elif context_feature_type == "categorical": - choices = env_bounds[k][3] - choice = np.random.choice(choices) - c[k] = choice - elif context_feature_type == "conditional": - condition = env_bounds[k][4] - choices = env_bounds[k][3][condition] - choice = np.random.choice(choices) - c[k] = choice - else: - c[k] = random_variable.rvs(size=1)[0] # sample variable - c[k] = np.clip(c[k], lower_bound, upper_bound) # check bounds - c[k] = context_feature_type(c[k]) # cast to given type - else: - # No special sampling rule for context feature k, use the default context feature value - c[k] = env_defaults[k] - contexts[i] = c - - return contexts diff --git a/carl/context/search_space_encoding.py b/carl/context/search_space_encoding.py new file mode 100644 index 00000000..893a71d4 --- /dev/null +++ b/carl/context/search_space_encoding.py @@ -0,0 +1,142 @@ +from __future__ import annotations + +from typing import Any, Union + +import json + +from ConfigSpace import ConfigurationSpace # type: ignore[import] +from ConfigSpace.read_and_write import json as csjson # type: ignore[import] +from omegaconf import DictConfig, ListConfig + + +class JSONCfgEncoder(json.JSONEncoder): + """Encode DictConfigs. + + Convert DictConfigs to normal dicts. + """ + + def default(self, obj: Union[DictConfig, ListConfig, Any]) -> Any: + """Modify default of JSON encoder + + Parameters + ---------- + obj : DictConfig | ListConfig | Any + Object to encode + + Returns + ------- + Any + Encoded object + """ + if isinstance(obj, DictConfig): + return dict(obj) + elif isinstance(obj, ListConfig): + parsed_list = [] + for o in obj: + if type(o) == DictConfig: + o = dict(o) + elif type(o) == ListConfig: + o = list(o) + parsed_list.append(o) + + return parsed_list # [dict(o) for o in obj] + return json.JSONEncoder.default(self, obj) + + +def search_space_to_config_space( + search_space: Union[str, DictConfig, ConfigurationSpace], seed: int = None +) -> ConfigurationSpace: + """ + Convert hydra search space to SMAC's configuration space. + + See the [ConfigSpace docs](https://automl.github.io/ConfigSpace/master/API-Doc.html#) for information of how + to define a configuration (search) space. + + In a yaml (hydra) config file, the smac.search space must take the form of: + + search_space: + hyperparameters: + hyperparameter_name_0: + key1: value1 + ... + hyperparameter_name_1: + key1: value1 + key2: value2 + ... + + + Parameters + ---------- + search_space : Union[str, DictConfig, ConfigurationSpace] + The search space, either a DictConfig from a hydra yaml config file, or a path to a json configuration space + file in the format required of ConfigSpace. + If it already is a ConfigurationSpace, just optionally seed it. + seed : Optional[int] + Optional seed to seed configuration space. + + + Example of a json-serialized ConfigurationSpace file. + { + "hyperparameters": [ + { + "name": "x0", + "type": "uniform_float", + "log": false, + "lower": -512.0, + "upper": 512.0, + "default": -3.0 + }, + { + "name": "x1", + "type": "uniform_float", + "log": false, + "lower": -512.0, + "upper": 512.0, + "default": -4.0 + } + ], + "conditions": [], + "forbiddens": [], + "python_module_version": "0.4.17", + "json_format_version": 0.2 + } + + + Returns + ------- + ConfigurationSpace + """ + if type(search_space) == str: + with open(search_space, "r") as f: + jason_string = f.read() + cs = csjson.read(jason_string) + elif type(search_space) == DictConfig: + # reorder hyperparameters as List[Dict] + hyperparameters = [] + for name, cfg in search_space.hyperparameters.items(): + cfg["name"] = name + if "default" not in cfg: + cfg["default"] = None + if "log" not in cfg: + cfg["log"] = False + hyperparameters.append(cfg) + search_space.hyperparameters = hyperparameters + + if "conditions" not in search_space: + search_space["conditions"] = [] + + if "forbiddens" not in search_space: + search_space["forbiddens"] = [] + + jason_string = json.dumps(search_space, cls=JSONCfgEncoder) + cs = csjson.read(jason_string) + elif type(search_space) == ConfigurationSpace: + cs = search_space + else: + raise ValueError( + f"search_space must be of type str or DictConfig. Got {type(search_space)}." + ) + + if seed is not None: + cs.seed(seed=seed) + return cs diff --git a/carl/context/selection.py b/carl/context/selection.py index 7be40f35..0ae1538f 100644 --- a/carl/context/selection.py +++ b/carl/context/selection.py @@ -122,6 +122,20 @@ def _select(self) -> Tuple[Context, int]: return context, self.context_id +class StaticSelector(AbstractSelector): + """ + Static selector. + + Does not change the context at all. + """ + + def _select(self) -> Tuple[Context, int]: + if self.context_id is None: + self.context_id = self.context_ids[0] + context = self.contexts[self.contexts_keys[self.context_id]] + return context, self.context_id + + class CustomSelector(AbstractSelector): """ Custom selector. diff --git a/carl/envs/__init__.py b/carl/envs/__init__.py index 829cb87c..e684c209 100644 --- a/carl/envs/__init__.py +++ b/carl/envs/__init__.py @@ -4,41 +4,50 @@ import warnings # Classic control is in gym and thus necessary for the base version to run -from carl.envs.classic_control import * +from carl.envs.gymnasium import * + + +def check_spec(spec_name: str) -> bool: + """Check if the spec is installed + + Parameters + ---------- + spec_name : str + Name of package that is necessary for the environment suite. + + Returns + ------- + bool + Whether the spec was found. + """ + spec = iutil.find_spec(spec_name) + found = spec is not None + if not found: + with warnings.catch_warnings(): + warnings.simplefilter("once") + warnings.warn( + f"Module {spec_name} not found. If you want to use these environments, please follow the installation guide." + ) + return found + # Environment loading -box2d_spec = iutil.find_spec("Box2D") -found = box2d_spec is not None +found = check_spec("Box2D") if found: - from carl.envs.box2d import * -else: - warnings.warn( - "Module 'Box2D' not found. If you want to use these environments, please follow the installation guide." - ) - -brax_spec = iutil.find_spec("brax") -found = brax_spec is not None + from carl.envs.gymnasium.box2d import * + +found = check_spec("brax") if found: from carl.envs.brax import * - pass -else: - warnings.warn( - "Module 'Brax' not found. If you want to use these environments, please follow the installation guide." - ) - -try: +found = check_spec("py4j") +if found: from carl.envs.mario import * -except: - warnings.warn( - "Module 'Mario' not found. Please follow installation guide for ToadGAN environment." - ) -dm_control_spec = iutil.find_spec("dm_control") -found = dm_control_spec is not None +found = check_spec("dm_control") if found: from carl.envs.dmc import * -else: - warnings.warn( - "Module 'dm_control' not found. If you want to use these environments, please follow the installation guide." - ) + +found = check_spec("distance") +if found: + from carl.envs.rna import * diff --git a/carl/envs/box2d/__init__.py b/carl/envs/box2d/__init__.py deleted file mode 100644 index ad6a3424..00000000 --- a/carl/envs/box2d/__init__.py +++ /dev/null @@ -1,22 +0,0 @@ -# flake8: noqa: F401 -from carl.envs.box2d.carl_bipedal_walker import ( - CONTEXT_BOUNDS as CARLBipedalWalkerEnv_bounds, -) -from carl.envs.box2d.carl_bipedal_walker import ( - DEFAULT_CONTEXT as CARLBipedalWalkerEnv_defaults, -) -from carl.envs.box2d.carl_bipedal_walker import CARLBipedalWalkerEnv - -# Contextenvs.s and bounds by name -from carl.envs.box2d.carl_lunarlander import CONTEXT_BOUNDS as CARLLunarLanderEnv_bounds -from carl.envs.box2d.carl_lunarlander import ( - DEFAULT_CONTEXT as CARLLunarLanderEnv_defaults, -) -from carl.envs.box2d.carl_lunarlander import CARLLunarLanderEnv -from carl.envs.box2d.carl_vehicle_racing import ( - CONTEXT_BOUNDS as CARLVehicleRacingEnv_bounds, -) -from carl.envs.box2d.carl_vehicle_racing import ( - DEFAULT_CONTEXT as CARLVehicleRacingEnv_defaults, -) -from carl.envs.box2d.carl_vehicle_racing import CARLVehicleRacingEnv diff --git a/carl/envs/box2d/carl_lunarlander.py b/carl/envs/box2d/carl_lunarlander.py deleted file mode 100644 index 8b8964b8..00000000 --- a/carl/envs/box2d/carl_lunarlander.py +++ /dev/null @@ -1,180 +0,0 @@ -from typing import Dict, List, Optional, Tuple, TypeVar, Union - -from gym import Wrapper -from gym.envs.box2d import lunar_lander - -from carl.context.selection import AbstractSelector -from carl.envs.carl_env import CARLEnv -from carl.utils.trial_logger import TrialLogger -from carl.utils.types import Context, Contexts - -ObsType = TypeVar("ObsType") -ActType = TypeVar("ActType") - -# import pyglet -# pyglet.options["shadow_window"] = False - -# TODO debug/test this environment by looking at rendering! - -DEFAULT_CONTEXT = { - "FPS": 50, - "SCALE": 30.0, # affects how fast-paced the game is, forces should be adjusted as well - # Engine powers - "MAIN_ENGINE_POWER": 13.0, - "SIDE_ENGINE_POWER": 0.6, - # random force on lunar lander body on reset - "INITIAL_RANDOM": 1000.0, # Set 1500 to make game harder - "GRAVITY_X": 0, - "GRAVITY_Y": -10, - # lunar lander body specification - "LEG_AWAY": 20, - "LEG_DOWN": 18, - "LEG_W": 2, - "LEG_H": 8, - "LEG_SPRING_TORQUE": 40, - "SIDE_ENGINE_HEIGHT": 14.0, - "SIDE_ENGINE_AWAY": 12.0, - # Size of world - "VIEWPORT_W": 600, - "VIEWPORT_H": 400, -} - -CONTEXT_BOUNDS = { - "FPS": (1, 500, float), - "SCALE": ( - 1, - 100, - float, - ), # affects how fast-paced the game is, forces should be adjusted as well - "MAIN_ENGINE_POWER": (0, 50, float), - "SIDE_ENGINE_POWER": (0, 50, float), - # random force on lunar lander body on reset - "INITIAL_RANDOM": (0, 2000, float), # Set 1500 to make game harder - "GRAVITY_X": (-20, 20, float), # unit: m/s² - "GRAVITY_Y": ( - -20, - -0.01, - float, - ), # the y-component of gravity must be smaller than 0 because otherwise the - # lunarlander leaves the frame by going up - # lunar lander body specification - "LEG_AWAY": (0, 50, float), - "LEG_DOWN": (0, 50, float), - "LEG_W": (1, 10, float), - "LEG_H": (1, 20, float), - "LEG_SPRING_TORQUE": (0, 100, float), - "SIDE_ENGINE_HEIGHT": (1, 20, float), - "SIDE_ENGINE_AWAY": (1, 20, float), - # Size of world - "VIEWPORT_W": (400, 1000, int), - "VIEWPORT_H": (200, 800, int), -} - - -class LunarLanderEnv(Wrapper): - def __init__( - self, - env: Optional[lunar_lander.LunarLander] = None, - high_gameover_penalty: bool = False, - ): - if env is None: - env = lunar_lander.LunarLander() - super().__init__(env=env) - - self.high_gameover_penalty = high_gameover_penalty - self.active_seed = None - - def step(self, action: ActType) -> Tuple[ObsType, float, bool, dict]: - self.env: lunar_lander.LunarLander - state, reward, done, info = self.env.step(action) - if self.env.game_over and self.high_gameover_penalty: - reward = -10000 - return state, reward, done, info - - def seed(self, seed: Optional[int] = None) -> Optional[int]: - seed_ = self.env.seed(seed) - self.active_seed = seed_[0] - return seed_ - - -class CARLLunarLanderEnv(CARLEnv): - def __init__( - self, - env: Optional[LunarLanderEnv] = None, - contexts: Contexts = {}, - hide_context: bool = True, - add_gaussian_noise_to_context: bool = False, - gaussian_noise_std_percentage: float = 0.05, - logger: Optional[TrialLogger] = None, - scale_context_features: str = "no", - default_context: Optional[Context] = DEFAULT_CONTEXT, - state_context_features: Optional[List[str]] = None, - context_mask: Optional[List[str]] = None, - max_episode_length: int = 1000, - high_gameover_penalty: bool = False, - dict_observation_space: bool = False, - context_selector: Optional[ - Union[AbstractSelector, type[AbstractSelector]] - ] = None, - context_selector_kwargs: Optional[Dict] = None, - ): - """ - - Parameters - ---------- - env: gym.Env, optional - Defaults to classic control environment mountain car from gym (MountainCarEnv). - contexts: List[Dict], optional - Different contexts / different environment parameter settings. - instance_mode: str, optional - """ - if env is None: - # env = lunar_lander.LunarLander() - env = LunarLanderEnv(high_gameover_penalty=high_gameover_penalty) - if not contexts: - contexts = {0: DEFAULT_CONTEXT} - super().__init__( - env=env, - contexts=contexts, - hide_context=hide_context, - add_gaussian_noise_to_context=add_gaussian_noise_to_context, - gaussian_noise_std_percentage=gaussian_noise_std_percentage, - logger=logger, - scale_context_features=scale_context_features, - default_context=default_context, - state_context_features=state_context_features, - max_episode_length=max_episode_length, - dict_observation_space=dict_observation_space, - context_selector=context_selector, - context_selector_kwargs=context_selector_kwargs, - context_mask=context_mask, - ) - self.whitelist_gaussian_noise = list( - DEFAULT_CONTEXT.keys() - ) # allow to augment all values - - def _update_context(self) -> None: - self.env: LunarLanderEnv - lunar_lander.FPS = self.context["FPS"] - lunar_lander.SCALE = self.context["SCALE"] - lunar_lander.MAIN_ENGINE_POWER = self.context["MAIN_ENGINE_POWER"] - lunar_lander.SIDE_ENGINE_POWER = self.context["SIDE_ENGINE_POWER"] - - lunar_lander.INITIAL_RANDOM = self.context["INITIAL_RANDOM"] - - lunar_lander.LEG_AWAY = self.context["LEG_AWAY"] - lunar_lander.LEG_DOWN = self.context["LEG_DOWN"] - lunar_lander.LEG_W = self.context["LEG_W"] - lunar_lander.LEG_H = self.context["LEG_H"] - lunar_lander.LEG_SPRING_TORQUE = self.context["LEG_SPRING_TORQUE"] - lunar_lander.SIDE_ENGINE_HEIGHT = self.context["SIDE_ENGINE_HEIGHT"] - lunar_lander.SIDE_ENGINE_AWAY = self.context["SIDE_ENGINE_AWAY"] - - lunar_lander.VIEWPORT_W = self.context["VIEWPORT_W"] - lunar_lander.VIEWPORT_H = self.context["VIEWPORT_H"] - - gravity_x = self.context["GRAVITY_X"] - gravity_y = self.context["GRAVITY_Y"] - - gravity = (gravity_x, gravity_y) - self.env.world.gravity = gravity diff --git a/carl/envs/box2d/carl_vehicle_racing.py b/carl/envs/box2d/carl_vehicle_racing.py deleted file mode 100644 index eb2ca06a..00000000 --- a/carl/envs/box2d/carl_vehicle_racing.py +++ /dev/null @@ -1,254 +0,0 @@ -from typing import Any, Dict, List, Optional, Tuple, Type, Union - -import numpy as np -import pyglet -from gym.envs.box2d import CarRacing -from gym.envs.box2d.car_dynamics import Car -from pyglet import gl - -from carl.context.selection import AbstractSelector -from carl.envs.box2d.parking_garage.bus import AWDBus # as Car -from carl.envs.box2d.parking_garage.bus import AWDBusLargeTrailer # as Car -from carl.envs.box2d.parking_garage.bus import AWDBusSmallTrailer # as Car -from carl.envs.box2d.parking_garage.bus import Bus # as Car -from carl.envs.box2d.parking_garage.bus import BusLargeTrailer # as Car -from carl.envs.box2d.parking_garage.bus import BusSmallTrailer # as Car -from carl.envs.box2d.parking_garage.bus import FWDBus # as Car -from carl.envs.box2d.parking_garage.bus import FWDBusLargeTrailer # as Car -from carl.envs.box2d.parking_garage.bus import FWDBusSmallTrailer # as Car -from carl.envs.box2d.parking_garage.race_car import AWDRaceCar # as Car -from carl.envs.box2d.parking_garage.race_car import AWDRaceCarLargeTrailer # as Car -from carl.envs.box2d.parking_garage.race_car import AWDRaceCarSmallTrailer # as Car -from carl.envs.box2d.parking_garage.race_car import FWDRaceCar # as Car -from carl.envs.box2d.parking_garage.race_car import FWDRaceCarLargeTrailer # as Car -from carl.envs.box2d.parking_garage.race_car import FWDRaceCarSmallTrailer # as Car -from carl.envs.box2d.parking_garage.race_car import RaceCarLargeTrailer # as Car -from carl.envs.box2d.parking_garage.race_car import RaceCarSmallTrailer # as Car -from carl.envs.box2d.parking_garage.race_car import RaceCar -from carl.envs.box2d.parking_garage.street_car import AWDStreetCar # as Car -from carl.envs.box2d.parking_garage.street_car import AWDStreetCarLargeTrailer # as Car -from carl.envs.box2d.parking_garage.street_car import AWDStreetCarSmallTrailer # as Car -from carl.envs.box2d.parking_garage.street_car import FWDStreetCar # as Car -from carl.envs.box2d.parking_garage.street_car import FWDStreetCarLargeTrailer # as Car -from carl.envs.box2d.parking_garage.street_car import FWDStreetCarSmallTrailer # as Car -from carl.envs.box2d.parking_garage.street_car import StreetCar # as Car -from carl.envs.box2d.parking_garage.street_car import StreetCarLargeTrailer # as Car -from carl.envs.box2d.parking_garage.street_car import StreetCarSmallTrailer # as Car -from carl.envs.box2d.parking_garage.trike import TukTuk # as Car -from carl.envs.box2d.parking_garage.trike import TukTukSmallTrailer # as Car -from carl.envs.carl_env import CARLEnv -from carl.utils.trial_logger import TrialLogger -from carl.utils.types import Context, Contexts, ObsType - -PARKING_GARAGE_DICT = { - # Racing car - "RaceCar": RaceCar, - "FWDRaceCar": FWDRaceCar, - "AWDRaceCar": AWDRaceCar, - "RaceCarSmallTrailer": RaceCarSmallTrailer, - "FWDRaceCarSmallTrailer": FWDRaceCarSmallTrailer, - "AWDRaceCarSmallTrailer": AWDRaceCarSmallTrailer, - "RaceCarLargeTrailer": RaceCarLargeTrailer, - "FWDRaceCarLargeTrailer": FWDRaceCarLargeTrailer, - "AWDRaceCarLargeTrailer": AWDRaceCarLargeTrailer, - # Street car - "StreetCar": StreetCar, - "FWDStreetCar": FWDStreetCar, - "AWDStreetCar": AWDStreetCar, - "StreetCarSmallTrailer": StreetCarSmallTrailer, - "FWDStreetCarSmallTrailer": FWDStreetCarSmallTrailer, - "AWDStreetCarSmallTrailer": AWDStreetCarSmallTrailer, - "StreetCarLargeTrailer": StreetCarLargeTrailer, - "FWDStreetCarLargeTrailer": FWDStreetCarLargeTrailer, - "AWDStreetCarLargeTrailer": AWDStreetCarLargeTrailer, - # Bus - "Bus": Bus, - "FWDBus": FWDBus, - "AWDBus": AWDBus, - "BusSmallTrailer": BusSmallTrailer, - "FWDBusSmallTrailer": FWDBusSmallTrailer, - "AWDBusSmallTrailer": AWDBusSmallTrailer, - "BusLargeTrailer": BusLargeTrailer, - "FWDBusLargeTrailer": FWDBusLargeTrailer, - "AWDBusLargeTrailer": AWDBusLargeTrailer, - # Tuk Tuk :) - "TukTuk": TukTuk, - "TukTukSmallTrailer": TukTukSmallTrailer, -} -PARKING_GARAGE = list(PARKING_GARAGE_DICT.values()) -VEHICLE_NAMES = list(PARKING_GARAGE_DICT.keys()) -DEFAULT_CONTEXT = { - "VEHICLE": PARKING_GARAGE.index(RaceCar), -} - -CONTEXT_BOUNDS = { - "VEHICLE": (None, None, "categorical", np.arange(0, len(PARKING_GARAGE))) -} -CATEGORICAL_CONTEXT_FEATURES = ["VEHICLE"] - - -class CustomCarRacingEnv(CarRacing): - def __init__(self, vehicle_class: Type[Car] = Car, verbose: bool = True): - super().__init__(verbose) - self.vehicle_class = vehicle_class - - def reset( - self, - *, - seed: Optional[int] = None, - return_info: bool = False, - options: Optional[dict] = None, - ) -> Union[ObsType, tuple[ObsType, dict]]: - self._destroy() - self.reward = 0.0 - self.prev_reward = 0.0 - self.tile_visited_count = 0 - self.t = 0.0 - self.road_poly: List[Tuple[List[float], Tuple[Any]]] = [] - - while True: - success = self._create_track() - if success: - break - if self.verbose == 1: - print( - "retry to generate track (normal if there are not many" - "instances of this message)" - ) - self.car = self.vehicle_class(self.world, *self.track[0][1:4]) # type: ignore [assignment] - - for i in range( - 49 - ): # this sets up the environment and resolves any initial violations of geometry - self.step(None) # type: ignore [arg-type] - - if not return_info: - return self.step(None)[0] # type: ignore [arg-type] - else: - return self.step(None)[0], {} # type: ignore [arg-type] - - def _render_indicators(self, W: int, H: int) -> None: - # copied from meta car racing - s = W / 40.0 - h = H / 40.0 - colors = [0, 0, 0, 1] * 4 - polygons = [W, 0, 0, W, 5 * h, 0, 0, 5 * h, 0, 0, 0, 0] - - def vertical_ind(place: int, val: int, color: Tuple) -> None: - colors.extend([color[0], color[1], color[2], 1] * 4) - polygons.extend( - [ - place * s, - h + h * val, - 0, - (place + 1) * s, - h + h * val, - 0, - (place + 1) * s, - h, - 0, - (place + 0) * s, - h, - 0, - ] - ) - - def horiz_ind(place: int, val: int, color: Tuple) -> None: - colors.extend([color[0], color[1], color[2], 1] * 4) - polygons.extend( - [ - (place + 0) * s, - 4 * h, - 0, - (place + val) * s, - 4 * h, - 0, - (place + val) * s, - 2 * h, - 0, - (place + 0) * s, - 2 * h, - 0, - ] - ) - - true_speed = np.sqrt( - np.square(self.car.hull.linearVelocity[0]) # type: ignore [attr-defined] - + np.square(self.car.hull.linearVelocity[1]) # type: ignore [attr-defined] - ) - - vertical_ind(5, 0.02 * true_speed, (1, 1, 1)) - - # Custom render to handle different amounts of wheels - vertical_ind(7, 0.01 * self.car.wheels[0].omega, (0.0, 0, 1)) # type: ignore [attr-defined] - for i in range(len(self.car.wheels)): # type: ignore [attr-defined] - vertical_ind(7 + i, 0.01 * self.car.wheels[i].omega, (0.0 + i * 0.1, 0, 1)) # type: ignore [attr-defined] - horiz_ind(20, -10.0 * self.car.wheels[0].joint.angle, (0, 1, 0)) # type: ignore [attr-defined] - horiz_ind(30, -0.8 * self.car.hull.angularVelocity, (1, 0, 0)) # type: ignore [attr-defined] - vl = pyglet.graphics.vertex_list( - len(polygons) // 3, ("v3f", polygons), ("c4f", colors) # gl.GL_QUADS, - ) - vl.draw(gl.GL_QUADS) - - -class CARLVehicleRacingEnv(CARLEnv): - def __init__( - self, - env: CustomCarRacingEnv = CustomCarRacingEnv(), - contexts: Optional[Contexts] = None, - hide_context: bool = True, - add_gaussian_noise_to_context: bool = False, - gaussian_noise_std_percentage: float = 0.01, - logger: Optional[TrialLogger] = None, - scale_context_features: str = "no", - default_context: Optional[Context] = DEFAULT_CONTEXT, - state_context_features: Optional[List[str]] = None, - context_mask: Optional[List[str]] = None, - dict_observation_space: bool = False, - context_selector: Optional[ - Union[AbstractSelector, type[AbstractSelector]] - ] = None, - context_selector_kwargs: Optional[Dict] = None, - ): - """ - - Parameters - ---------- - env: gym.Env, optional - Defaults to classic control environment mountain car from gym (MountainCarEnv). - contexts: List[Dict], optional - Different contexts / different environment parameter settings. - instance_mode: str, optional - """ - if not hide_context: - raise NotImplementedError( - "The context is already coded in the pixel state, the context cannot be hidden that easily. " - "Due to the pixel state we cannot easily concatenate the context to the state, therefore " - "hide_context must be True but at the same time the context is visible via the pixel state." - ) - - if not contexts: - contexts = {0: DEFAULT_CONTEXT} - super().__init__( - env=env, - contexts=contexts, - hide_context=hide_context, - add_gaussian_noise_to_context=add_gaussian_noise_to_context, - gaussian_noise_std_percentage=gaussian_noise_std_percentage, - logger=logger, - scale_context_features=scale_context_features, - default_context=default_context, - state_context_features=state_context_features, - dict_observation_space=dict_observation_space, - context_selector=context_selector, - context_selector_kwargs=context_selector_kwargs, - context_mask=context_mask, - ) - self.whitelist_gaussian_noise = [ - k for k in DEFAULT_CONTEXT.keys() if k not in CATEGORICAL_CONTEXT_FEATURES - ] - - def _update_context(self) -> None: - self.env: CustomCarRacingEnv - vehicle_class_index = self.context["VEHICLE"] - self.env.vehicle_class = PARKING_GARAGE[vehicle_class_index] diff --git a/carl/envs/box2d/utils.py b/carl/envs/box2d/utils.py deleted file mode 100644 index 82c9e51e..00000000 --- a/carl/envs/box2d/utils.py +++ /dev/null @@ -1,12 +0,0 @@ -from typing import List - -import Box2D - - -def safe_destroy(world: Box2D.b2World, bodies: List[Box2D.b2Body]) -> None: - for body in bodies: - try: - world.DestroyBody(body) - except AssertionError as error: - if str(error) != "m_bodyCount > 0": - raise error diff --git a/carl/envs/brax/__init__.py b/carl/envs/brax/__init__.py index eee221fb..ed4b23ed 100644 --- a/carl/envs/brax/__init__.py +++ b/carl/envs/brax/__init__.py @@ -1,20 +1,24 @@ # flake8: noqa: F401 -# Contexts and bounds by name -from carl.envs.brax.carl_ant import CONTEXT_BOUNDS as CARLAnt_bounds -from carl.envs.brax.carl_ant import DEFAULT_CONTEXT as CARLAnt_defaults -from carl.envs.brax.carl_ant import CARLAnt -from carl.envs.brax.carl_fetch import CONTEXT_BOUNDS as CARLFetch_bounds -from carl.envs.brax.carl_fetch import DEFAULT_CONTEXT as CARLFetch_defaults -from carl.envs.brax.carl_fetch import CARLFetch -from carl.envs.brax.carl_grasp import CONTEXT_BOUNDS as CARLGrasp_bounds -from carl.envs.brax.carl_grasp import DEFAULT_CONTEXT as CARLGrasp_defaults -from carl.envs.brax.carl_grasp import CARLGrasp -from carl.envs.brax.carl_halfcheetah import CONTEXT_BOUNDS as CARLHalfcheetah_bounds -from carl.envs.brax.carl_halfcheetah import DEFAULT_CONTEXT as CARLHalfcheetah_defaults -from carl.envs.brax.carl_halfcheetah import CARLHalfcheetah -from carl.envs.brax.carl_humanoid import CONTEXT_BOUNDS as CARLHumanoid_bounds -from carl.envs.brax.carl_humanoid import DEFAULT_CONTEXT as CARLHumanoid_defaults -from carl.envs.brax.carl_humanoid import CARLHumanoid -from carl.envs.brax.carl_ur5e import CONTEXT_BOUNDS as CARLUr5e_bounds -from carl.envs.brax.carl_ur5e import DEFAULT_CONTEXT as CARLUr5e_defaults -from carl.envs.brax.carl_ur5e import CARLUr5e +from carl.envs.brax.carl_ant import CARLBraxAnt +from carl.envs.brax.carl_halfcheetah import CARLBraxHalfcheetah +from carl.envs.brax.carl_hopper import CARLBraxHopper +from carl.envs.brax.carl_humanoid import CARLBraxHumanoid +from carl.envs.brax.carl_humanoidstandup import CARLBraxHumanoidStandup +from carl.envs.brax.carl_inverted_double_pendulum import CARLBraxInvertedDoublePendulum +from carl.envs.brax.carl_inverted_pendulum import CARLBraxInvertedPendulum +from carl.envs.brax.carl_pusher import CARLBraxPusher +from carl.envs.brax.carl_reacher import CARLBraxReacher +from carl.envs.brax.carl_walker2d import CARLBraxWalker2d + +__all__ = [ + "CARLBraxAnt", + "CARLBraxHalfcheetah", + "CARLBraxHopper", + "CARLBraxHumanoid", + "CARLBraxHumanoidStandup", + "CARLBraxInvertedDoublePendulum", + "CARLBraxInvertedPendulum", + "CARLBraxPusher", + "CARLBraxReacher", + "CARLBraxWalker2d", +] diff --git a/carl/envs/brax/carl_ant.py b/carl/envs/brax/carl_ant.py index c53fd64f..e8bb6d7c 100644 --- a/carl/envs/brax/carl_ant.py +++ b/carl/envs/brax/carl_ant.py @@ -1,113 +1,34 @@ -from typing import Any, Dict, List, Optional, Union +from __future__ import annotations -import copy -import json - -import brax import numpy as np -from brax.envs.ant import _SYSTEM_CONFIG, Ant -from brax.envs.wrappers import GymWrapper, VectorGymWrapper, VectorWrapper -from google.protobuf import json_format, text_format -from google.protobuf.json_format import MessageToDict -from numpyencoder import NumpyEncoder - -from carl.context.selection import AbstractSelector -from carl.envs.carl_env import CARLEnv -from carl.utils.trial_logger import TrialLogger -from carl.utils.types import Context, Contexts - -DEFAULT_CONTEXT = { - "joint_stiffness": 5000, - "gravity": -9.8, - "friction": 0.6, - "angular_damping": -0.05, - "actuator_strength": 300, - "joint_angular_damping": 35, - "torso_mass": 10, -} - -CONTEXT_BOUNDS = { - "joint_stiffness": (1, np.inf, float), - "gravity": (-np.inf, -0.1, float), - "friction": (-np.inf, np.inf, float), - "angular_damping": (-np.inf, np.inf, float), - "actuator_strength": (1, np.inf, float), - "joint_angular_damping": (0, np.inf, float), - "torso_mass": (0.1, np.inf, float), -} - - -class CARLAnt(CARLEnv): - def __init__( - self, - env: Ant = Ant(), - n_envs: int = 1, - contexts: Contexts = {}, - hide_context: bool = False, - add_gaussian_noise_to_context: bool = False, - gaussian_noise_std_percentage: float = 0.01, - logger: Optional[TrialLogger] = None, - scale_context_features: str = "no", - default_context: Optional[Context] = DEFAULT_CONTEXT, - state_context_features: Optional[List[str]] = None, - context_mask: Optional[List[str]] = None, - dict_observation_space: bool = False, - context_selector: Optional[ - Union[AbstractSelector, type[AbstractSelector]] - ] = None, - context_selector_kwargs: Optional[Dict] = None, - ): - if n_envs == 1: - env = GymWrapper(env) - else: - env = VectorGymWrapper(VectorWrapper(env, n_envs)) - - self.base_config = MessageToDict( - text_format.Parse(_SYSTEM_CONFIG, brax.Config()) - ) - if not contexts: - contexts = {0: DEFAULT_CONTEXT} - super().__init__( - env=env, - n_envs=n_envs, - contexts=contexts, - hide_context=hide_context, - add_gaussian_noise_to_context=add_gaussian_noise_to_context, - gaussian_noise_std_percentage=gaussian_noise_std_percentage, - logger=logger, - scale_context_features=scale_context_features, - default_context=default_context, - state_context_features=state_context_features, - dict_observation_space=dict_observation_space, - context_selector=context_selector, - context_selector_kwargs=context_selector_kwargs, - context_mask=context_mask, - ) - self.whitelist_gaussian_noise = list( - DEFAULT_CONTEXT.keys() - ) # allow to augment all values - - def _update_context(self) -> None: - self.env: Ant - config = copy.deepcopy(self.base_config) - config["gravity"] = {"z": self.context["gravity"]} - config["friction"] = self.context["friction"] - config["angularDamping"] = self.context["angular_damping"] - for j in range(len(config["joints"])): - config["joints"][j]["angularDamping"] = self.context[ - "joint_angular_damping" - ] - config["joints"][j]["stiffness"] = self.context["joint_stiffness"] - for a in range(len(config["actuators"])): - config["actuators"][a]["strength"] = self.context["actuator_strength"] - config["bodies"][0]["mass"] = self.context["torso_mass"] - # This converts the dict to a JSON String, then parses it into an empty brax config - self.env.sys = brax.System( - json_format.Parse(json.dumps(config, cls=NumpyEncoder), brax.Config()) - ) - def __getattr__(self, name: str) -> Any: - if name in ["sys", "__getstate__"]: - return getattr(self.env._environment, name) - else: - return getattr(self, name) +from carl.context.context_space import ContextFeature, UniformFloatContextFeature +from carl.envs.brax.carl_brax_env import CARLBraxEnv + + +class CARLBraxAnt(CARLBraxEnv): + env_name: str = "ant" + asset_path: str = "envs/assets/ant.xml" + + @staticmethod + def get_context_features() -> dict[str, ContextFeature]: + return { + "gravity": UniformFloatContextFeature( + "gravity", lower=-1000, upper=-1e-6, default_value=-9.8 + ), + "friction": UniformFloatContextFeature( + "friction", lower=0, upper=100, default_value=1 + ), + "elasticity": UniformFloatContextFeature( + "elasticity", lower=0, upper=100, default_value=0 + ), + "ang_damping": UniformFloatContextFeature( + "ang_damping", lower=-np.inf, upper=np.inf, default_value=-0.05 + ), + "mass_torso": UniformFloatContextFeature( + "mass_torso", lower=1e-6, upper=np.inf, default_value=10 + ), + "viscosity": UniformFloatContextFeature( + "viscosity", lower=0, upper=np.inf, default_value=0 + ), + } diff --git a/carl/envs/brax/carl_brax_env.py b/carl/envs/brax/carl_brax_env.py new file mode 100644 index 00000000..b03dfd93 --- /dev/null +++ b/carl/envs/brax/carl_brax_env.py @@ -0,0 +1,254 @@ +from __future__ import annotations + +from typing import Any + +from dataclasses import asdict + +import brax +import gymnasium +import numpy as np +from brax.base import Geometry, Inertia, Link, System +from brax.io import mjcf +from etils import epath +from jax import numpy as jp + +from carl.context.selection import AbstractSelector +from carl.envs.brax.wrappers import GymWrapper, VectorGymWrapper +from carl.envs.carl_env import CARLEnv +from carl.utils.types import Contexts + + +def set_geom_attr( + geom: Geometry, data: dict[str, Any], context: dict[str, Any], key: str +) -> dict: + """Set Geometry attribute + + Check whether the desired attribute is present both in the geometry and in the context. + + Parameters + ---------- + geom : Geometry + Brax geometry (a surface or spatial volume with a shape and material properties) + data : dict[str, Any] + Data from the Geometry dataclass, potentially already modified. + context : dict[str, Any] + The context to set. + key : str + The context feature to update. + + Returns + ------- + dict + Modified data from the geometry dataclas. + """ + if key in context and key in data: + value = getattr(geom, key) + n_items = len(value) + vec = jp.array([context[key]] * n_items) + data[key] = vec + return data + + +def set_masses2(sys: System, context: dict[str, Any]) -> System: + for cfname, cfvalue in context.items(): + if cfname.startswith("mass"): + link_name = cfname.split("_")[-1] + if link_name in sys.link_names: + idx = sys.link_names.index(link_name) + sys.link.inertia.mass.at[idx].set(cfvalue) + return sys + + +def _set_masses( + context: dict[str, Any], inertia: Inertia, link_names: list[str] +) -> Inertia: + """Actual/helper method to set masses + + The required syntax for masses is as follows: + `mass_` where linkname is the name of the entity to update, e.g. torso. + + Parameters + ---------- + context : dict[str, Any] + Context to set + inertia : Inertia + The inertia dataclass. + link_names : list[str] + Available link names. + + Raises + ------ + RuntimeError + When link name not in available names + + Returns + ------- + Inertia + Update inertia dataclass. + """ + inertia_data = asdict(inertia) + for cfname, cfvalue in context.items(): + if cfname.startswith("mass"): + link_name = cfname.split("_", 1)[-1] + if link_name in link_names: + idx = link_names.index(link_name) + inertia_data["mass"] = inertia_data["mass"].at[idx].set(cfvalue) + else: + raise RuntimeError( + f"Link {link_name} not in available link names {link_names}. Probably " + "something went wrong during context creation." + ) + inertia_new = Inertia(**inertia_data) + return inertia_new + + +def set_masses(sys: System, context: dict[str, Any]) -> System: + """Set masses + + The required syntax for masses is as follows: + `mass_` where linkname is the name of the entity to update, e.g. torso. + + Parameters + ---------- + sys : System + The brax system definition. + context : dict[str, Any] + Context to set. + + Returns + ------- + System + The updated system. + """ + link_data = asdict(sys.link) + inertia_new = _set_masses(context, sys.link.inertia, sys.link_names) + link_data["inertia"] = inertia_new + link_new = Link(**link_data) + sys = sys.replace(link=link_new) + return sys + + +def check_context( + context: dict[str, Any], registered_context_features: list[str] +) -> None: + for cfname in context.keys(): + if cfname not in registered_context_features and not cfname.startswith("mass_"): + raise RuntimeError( + f"Context feature {cfname} can not be updated in the brax system. Only " + f"{registered_context_features} are possible." + ) + + +class CARLBraxEnv(CARLEnv): + env_name: str + backend: str = "spring" + + def __init__( + self, + env: brax.envs.env.Env | None = None, + batch_size: int = 1, + contexts: Contexts | None = None, + obs_context_features: list[str] | None = None, + obs_context_as_dict: bool = True, + context_selector: AbstractSelector | type[AbstractSelector] | None = None, + context_selector_kwargs: dict = None, + **kwargs, + ) -> None: + """ + CARL Gymnasium Environment. + + Parameters + ---------- + + env : brax.envs.env.Env | None + Brax environment, the default is None. + If None, instantiate the env with brax' make function and + `self.env_name` which is defined in each child class. + batch_size : int + Number of environments to batch together, by default 1. + contexts : Contexts | None, optional + Context set, by default None. If it is None, we build the + context set with the default context. + obs_context_features : list[str] | None, optional + Context features which should be included in the observation, by default None. + If they are None, add all context features. + context_selector: AbstractSelector | type[AbstractSelector] | None, optional + The context selector (class), after each reset selects a new context to use. + If None, use a round robin selector. + context_selector_kwargs : dict, optional + Optional keyword arguments for the context selector, by default None. + Only used when `context_selector` is not None. + + Attributes + ---------- + env_name: str + The registered gymnasium environment name. + backend: str + + """ + if env is None: + bs = batch_size if batch_size != 1 else None + env = brax.envs.create( + env_name=self.env_name, backend=self.backend, batch_size=bs + ) + # Brax uses gym instead of gymnasium + if batch_size == 1: + env = GymWrapper(env) + else: + env = VectorGymWrapper(env) + + # The observation space also needs to from gymnasium + env.observation_space = gymnasium.spaces.Box( + low=env.observation_space.low, + high=env.observation_space.high, + dtype=np.float32, + ) + + super().__init__( + env=env, + contexts=contexts, + obs_context_features=obs_context_features, + obs_context_as_dict=obs_context_as_dict, + context_selector=context_selector, + context_selector_kwargs=context_selector_kwargs, + **kwargs, + ) + + def _update_context(self) -> None: + context = self.context + + # Those context features can be updated + every feature starting with `mass_` + registered_cfs = [ + "friction", + "ang_damping", + "gravity", + "viscosity", + "elasticity", + ] + check_context(context, registered_cfs) + + path = epath.resource_path("brax") / self.asset_path + sys = mjcf.load(path) + + if "gravity" in context: + sys = sys.replace(gravity=jp.array([0, 0, self.context["gravity"]])) + if "ang_damping" in context: + sys = sys.replace(ang_damping=self.context["ang_damping"]) + if "viscosity" in context: + sys = sys.replace(ang_damping=self.context["viscosity"]) + + sys = set_masses(sys, context) + + if "friction" in context or "elasticity" in context: + updated_geoms = [] + for i, geom in enumerate(sys.geoms): + cls = type(geom) + data = asdict(geom) + data = set_geom_attr(geom, data, context, "friction") + data = set_geom_attr(geom, data, context, "elasticity") + + geom_new = cls(**data) + updated_geoms.append(geom_new) + sys = sys.replace(geoms=updated_geoms) + + self.env.sys = sys diff --git a/carl/envs/brax/carl_fetch.py b/carl/envs/brax/carl_fetch.py deleted file mode 100644 index 272e6481..00000000 --- a/carl/envs/brax/carl_fetch.py +++ /dev/null @@ -1,127 +0,0 @@ -from typing import Any, Dict, List, Optional, Union - -import copy -import json - -import brax -import numpy as np -from brax.envs.fetch import _SYSTEM_CONFIG, Fetch -from brax.envs.wrappers import GymWrapper, VectorGymWrapper, VectorWrapper -from google.protobuf import json_format, text_format -from google.protobuf.json_format import MessageToDict -from numpyencoder import NumpyEncoder - -from carl.context.selection import AbstractSelector -from carl.envs.carl_env import CARLEnv -from carl.utils.trial_logger import TrialLogger -from carl.utils.types import Context, Contexts - -DEFAULT_CONTEXT = { - "joint_stiffness": 5000, - "gravity": -9.8, - "friction": 0.6, - "angular_damping": -0.05, # Angular velocity damping applied to each body - "actuator_strength": 300, - "joint_angular_damping": 35, # Damps parent and child angular velocities to be equal - "torso_mass": 1, - "target_radius": 2, - "target_distance": 15, -} - -CONTEXT_BOUNDS = { - "joint_stiffness": (1, np.inf, float), - "gravity": (-np.inf, -0.1, float), - "friction": (-np.inf, np.inf, float), - "angular_damping": (-np.inf, np.inf, float), - "actuator_strength": (1, np.inf, float), - "joint_angular_damping": (0, np.inf, float), - "torso_mass": (0.1, np.inf, float), - "target_radius": (0.1, np.inf, float), - "target_distance": (0.1, np.inf, float), -} - - -class CARLFetch(CARLEnv): - def __init__( - self, - env: Fetch = Fetch(), - n_envs: int = 1, - contexts: Contexts = {}, - hide_context: bool = False, - add_gaussian_noise_to_context: bool = False, - gaussian_noise_std_percentage: float = 0.01, - logger: Optional[TrialLogger] = None, - scale_context_features: str = "no", - default_context: Optional[Context] = DEFAULT_CONTEXT, - state_context_features: Optional[List[str]] = None, - context_mask: Optional[List[str]] = None, - dict_observation_space: bool = False, - context_selector: Optional[ - Union[AbstractSelector, type[AbstractSelector]] - ] = None, - context_selector_kwargs: Optional[Dict] = None, - ): - if n_envs == 1: - env = GymWrapper(env) - else: - env = VectorGymWrapper(VectorWrapper(env, n_envs)) - - self.base_config = MessageToDict( - text_format.Parse(_SYSTEM_CONFIG, brax.Config()) - ) - if not contexts: - contexts = {0: DEFAULT_CONTEXT} - super().__init__( - env=env, - n_envs=n_envs, - contexts=contexts, - hide_context=hide_context, - add_gaussian_noise_to_context=add_gaussian_noise_to_context, - gaussian_noise_std_percentage=gaussian_noise_std_percentage, - logger=logger, - scale_context_features=scale_context_features, - default_context=default_context, - state_context_features=state_context_features, - dict_observation_space=dict_observation_space, - context_selector=context_selector, - context_selector_kwargs=context_selector_kwargs, - context_mask=context_mask, - ) - self.whitelist_gaussian_noise = list( - DEFAULT_CONTEXT.keys() - ) # allow to augment all values - - def _update_context(self) -> None: - self.env: Fetch - config = copy.deepcopy(self.base_config) - config["gravity"] = {"z": self.context["gravity"]} - config["friction"] = self.context["friction"] - config["angularDamping"] = self.context["angular_damping"] - for j in range(len(config["joints"])): - config["joints"][j]["angularDamping"] = self.context[ - "joint_angular_damping" - ] - config["joints"][j]["stiffness"] = self.context["joint_stiffness"] - for a in range(len(config["actuators"])): - config["actuators"][a]["strength"] = self.context["actuator_strength"] - config["bodies"][0]["mass"] = self.context["torso_mass"] - # This converts the dict to a JSON String, then parses it into an empty brax config - self.env.sys = brax.System( - json_format.Parse(json.dumps(config, cls=NumpyEncoder), brax.Config()) - ) - self.env.target_idx = self.env.sys.body.index["Target"] - self.env.torso_idx = self.env.sys.body.index["Torso"] - self.env.target_radius = self.context["target_radius"] - self.env.target_distance = self.context["target_distance"] - - def __getattr__(self, name: str) -> Any: - if name in [ - "sys", - "target_distance", - "target_radius", - "target_idx", - "torso_idx", - ]: - return getattr(self.env._environment, name) - else: - return getattr(self, name) diff --git a/carl/envs/brax/carl_grasp.py b/carl/envs/brax/carl_grasp.py deleted file mode 100644 index f7795a42..00000000 --- a/carl/envs/brax/carl_grasp.py +++ /dev/null @@ -1,132 +0,0 @@ -from typing import Any, Dict, List, Optional, Union - -import copy -import json - -import brax -import numpy as np -from brax.envs.grasp import _SYSTEM_CONFIG, Grasp -from brax.envs.wrappers import GymWrapper, VectorGymWrapper, VectorWrapper -from google.protobuf import json_format, text_format -from google.protobuf.json_format import MessageToDict -from numpyencoder import NumpyEncoder - -from carl.context.selection import AbstractSelector -from carl.envs.carl_env import CARLEnv -from carl.utils.trial_logger import TrialLogger -from carl.utils.types import Context, Contexts - -DEFAULT_CONTEXT = { - "joint_stiffness": 5000, - "gravity": -9.8, - "friction": 0.6, - "angular_damping": -0.05, - "actuator_strength": 300, - "joint_angular_damping": 50, - "target_radius": 1.1, - "target_distance": 10.0, - "target_height": 8.0, -} - -CONTEXT_BOUNDS = { - "joint_stiffness": (1, np.inf, float), - "gravity": (-np.inf, -0.1, float), - "friction": (-np.inf, np.inf, float), - "angular_damping": (-np.inf, np.inf, float), - "actuator_strength": (1, np.inf, float), - "joint_angular_damping": (0, np.inf, float), - "target_radius": (0.1, np.inf, float), - "target_distance": (0.1, np.inf, float), - "target_height": (0.1, np.inf, float), -} - - -class CARLGrasp(CARLEnv): - def __init__( - self, - env: Grasp = Grasp(), - n_envs: int = 1, - contexts: Contexts = {}, - hide_context: bool = False, - add_gaussian_noise_to_context: bool = False, - gaussian_noise_std_percentage: float = 0.01, - logger: Optional[TrialLogger] = None, - scale_context_features: str = "no", - default_context: Optional[Context] = DEFAULT_CONTEXT, - state_context_features: Optional[List[str]] = None, - context_mask: Optional[List[str]] = None, - dict_observation_space: bool = False, - context_selector: Optional[ - Union[AbstractSelector, type[AbstractSelector]] - ] = None, - context_selector_kwargs: Optional[Dict] = None, - ): - if n_envs == 1: - env = GymWrapper(env) - else: - env = VectorGymWrapper(VectorWrapper(env, n_envs)) - - self.base_config = MessageToDict( - text_format.Parse(_SYSTEM_CONFIG, brax.Config()) - ) - if not contexts: - contexts = {0: DEFAULT_CONTEXT} - super().__init__( - env=env, - n_envs=n_envs, - contexts=contexts, - hide_context=hide_context, - add_gaussian_noise_to_context=add_gaussian_noise_to_context, - gaussian_noise_std_percentage=gaussian_noise_std_percentage, - logger=logger, - scale_context_features=scale_context_features, - default_context=default_context, - state_context_features=state_context_features, - dict_observation_space=dict_observation_space, - context_selector=context_selector, - context_selector_kwargs=context_selector_kwargs, - context_mask=context_mask, - ) - self.whitelist_gaussian_noise = list( - DEFAULT_CONTEXT.keys() - ) # allow to augment all values - - def _update_context(self) -> None: - self.env: Grasp - config = copy.deepcopy(self.base_config) - config["gravity"] = {"z": self.context["gravity"]} - config["friction"] = self.context["friction"] - config["angularDamping"] = self.context["angular_damping"] - for j in range(len(config["joints"])): - config["joints"][j]["angularDamping"] = self.context[ - "joint_angular_damping" - ] - config["joints"][j]["stiffness"] = self.context["joint_stiffness"] - for a in range(len(config["actuators"])): - config["actuators"][a]["strength"] = self.context["actuator_strength"] - # This converts the dict to a JSON String, then parses it into an empty brax config - self.env.sys = brax.System( - json_format.Parse(json.dumps(config, cls=NumpyEncoder), brax.Config()) - ) - self.env.object_idx = self.env.sys.body.index["Object"] - self.env.target_idx = self.env.sys.body.index["Target"] - self.env.hand_idx = self.env.sys.body.index["HandThumbProximal"] - self.env.palm_idx = self.env.sys.body.index["HandPalm"] - self.env.target_radius = self.context["target_radius"] - self.env.target_distance = self.context["target_distance"] - self.env.target_height = self.context["target_height"] - - def __getattr__(self, name: str) -> Any: - if name in [ - "sys", - "object_idx", - "target_idx", - "hand_idx", - "palm_idx", - "target_radius", - "target_distance", - "target_height", - ]: - return getattr(self.env._environment, name) - else: - return getattr(self, name) diff --git a/carl/envs/brax/carl_halfcheetah.py b/carl/envs/brax/carl_halfcheetah.py index aa014088..c1a69e46 100644 --- a/carl/envs/brax/carl_halfcheetah.py +++ b/carl/envs/brax/carl_halfcheetah.py @@ -1,109 +1,52 @@ -from typing import Any, Dict, List, Optional, Union +from __future__ import annotations -import copy -import json - -import brax import numpy as np -from brax.envs.half_cheetah import _SYSTEM_CONFIG, Halfcheetah -from brax.envs.wrappers import GymWrapper, VectorGymWrapper, VectorWrapper -from google.protobuf import json_format, text_format -from google.protobuf.json_format import MessageToDict -from numpyencoder import NumpyEncoder - -from carl.context.selection import AbstractSelector -from carl.envs.carl_env import CARLEnv -from carl.utils.trial_logger import TrialLogger -from carl.utils.types import Context, Contexts - -DEFAULT_CONTEXT = { - "joint_stiffness": 15000.0, - "gravity": -9.8, - "friction": 0.6, - "angular_damping": -0.05, - "joint_angular_damping": 20, - "torso_mass": 9.457333, -} - -CONTEXT_BOUNDS = { - "joint_stiffness": (1, np.inf, float), - "gravity": (-np.inf, -0.1, float), - "friction": (-np.inf, np.inf, float), - "angular_damping": (-np.inf, np.inf, float), - "joint_angular_damping": (0, np.inf, float), - "torso_mass": (0.1, np.inf, float), -} - - -class CARLHalfcheetah(CARLEnv): - def __init__( - self, - env: Halfcheetah = Halfcheetah(), - n_envs: int = 1, - contexts: Contexts = {}, - hide_context: bool = False, - add_gaussian_noise_to_context: bool = False, - gaussian_noise_std_percentage: float = 0.01, - logger: Optional[TrialLogger] = None, - scale_context_features: str = "no", - default_context: Optional[Context] = DEFAULT_CONTEXT, - state_context_features: Optional[List[str]] = None, - context_mask: Optional[List[str]] = None, - dict_observation_space: bool = False, - context_selector: Optional[ - Union[AbstractSelector, type[AbstractSelector]] - ] = None, - context_selector_kwargs: Optional[Dict] = None, - ): - if n_envs == 1: - env = GymWrapper(env) - else: - env = VectorGymWrapper(VectorWrapper(env, n_envs)) - - self.base_config = MessageToDict( - text_format.Parse(_SYSTEM_CONFIG, brax.Config()) - ) - if not contexts: - contexts = {0: DEFAULT_CONTEXT} - super().__init__( - env=env, - n_envs=n_envs, - contexts=contexts, - hide_context=hide_context, - add_gaussian_noise_to_context=add_gaussian_noise_to_context, - gaussian_noise_std_percentage=gaussian_noise_std_percentage, - logger=logger, - scale_context_features=scale_context_features, - default_context=default_context, - state_context_features=state_context_features, - dict_observation_space=dict_observation_space, - context_selector=context_selector, - context_selector_kwargs=context_selector_kwargs, - context_mask=context_mask, - ) - self.whitelist_gaussian_noise = list( - DEFAULT_CONTEXT.keys() - ) # allow to augment all values - - def _update_context(self) -> None: - self.env: Halfcheetah - config = copy.deepcopy(self.base_config) - config["gravity"] = {"z": self.context["gravity"]} - config["friction"] = self.context["friction"] - config["angularDamping"] = self.context["angular_damping"] - for j in range(len(config["joints"])): - config["joints"][j]["angularDamping"] = self.context[ - "joint_angular_damping" - ] - config["joints"][j]["stiffness"] = self.context["joint_stiffness"] - config["bodies"][0]["mass"] = self.context["torso_mass"] - # This converts the dict to a JSON String, then parses it into an empty brax config - self.env.sys = brax.System( - json_format.Parse(json.dumps(config, cls=NumpyEncoder), brax.Config()) - ) - def __getattr__(self, name: str) -> Any: - if name in ["sys"]: - return getattr(self.env._environment, name) - else: - return getattr(self, name) +from carl.context.context_space import ContextFeature, UniformFloatContextFeature +from carl.envs.brax.carl_brax_env import CARLBraxEnv + + +class CARLBraxHalfcheetah(CARLBraxEnv): + env_name: str = "halfcheetah" + asset_path: str = "envs/assets/half_cheetah.xml" + + @staticmethod + def get_context_features() -> dict[str, ContextFeature]: + return { + "gravity": UniformFloatContextFeature( + "gravity", lower=-1000, upper=-1e-6, default_value=-9.8 + ), + "friction": UniformFloatContextFeature( + "friction", lower=0, upper=100, default_value=1 + ), + "elasticity": UniformFloatContextFeature( + "elasticity", lower=0, upper=100, default_value=0 + ), + "ang_damping": UniformFloatContextFeature( + "ang_damping", lower=-np.inf, upper=np.inf, default_value=-0.05 + ), + "viscosity": UniformFloatContextFeature( + "viscosity", lower=0, upper=np.inf, default_value=0 + ), + "mass_torso": UniformFloatContextFeature( + "mass_torso", lower=1e-6, upper=np.inf, default_value=10 + ), + "mass_bthigh": UniformFloatContextFeature( + "mass_bthigh", lower=1e-6, upper=np.inf, default_value=1.5435146 + ), + "mass_bshin": UniformFloatContextFeature( + "mass_bshin", lower=1e-6, upper=np.inf, default_value=1.5874476 + ), + "mass_bfoot": UniformFloatContextFeature( + "mass_bfoot", lower=1e-6, upper=np.inf, default_value=1.0953975 + ), + "mass_fthigh": UniformFloatContextFeature( + "mass_fthigh", lower=1e-6, upper=np.inf, default_value=1.4380753 + ), + "mass_fshin": UniformFloatContextFeature( + "mass_fshin", lower=1e-6, upper=np.inf, default_value=1.2008368 + ), + "mass_ffoot": UniformFloatContextFeature( + "mass_ffoot", lower=1e-6, upper=np.inf, default_value=0.8845188 + ), + } diff --git a/carl/envs/brax/carl_hopper.py b/carl/envs/brax/carl_hopper.py new file mode 100644 index 00000000..be9c1699 --- /dev/null +++ b/carl/envs/brax/carl_hopper.py @@ -0,0 +1,43 @@ +from __future__ import annotations + +import numpy as np + +from carl.context.context_space import ContextFeature, UniformFloatContextFeature +from carl.envs.brax.carl_brax_env import CARLBraxEnv + + +class CARLBraxHopper(CARLBraxEnv): + env_name: str = "hopper" + asset_path: str = "envs/assets/hopper.xml" + + @staticmethod + def get_context_features() -> dict[str, ContextFeature]: + return { + "gravity": UniformFloatContextFeature( + "gravity", lower=-1000, upper=-1e-6, default_value=-9.8 + ), + "friction": UniformFloatContextFeature( + "friction", lower=0, upper=100, default_value=1 + ), + "elasticity": UniformFloatContextFeature( + "elasticity", lower=0, upper=100, default_value=0 + ), + "ang_damping": UniformFloatContextFeature( + "ang_damping", lower=-np.inf, upper=np.inf, default_value=-0.05 + ), + "viscosity": UniformFloatContextFeature( + "viscosity", lower=0, upper=np.inf, default_value=0 + ), + "mass_torso": UniformFloatContextFeature( + "mass_torso", lower=1e-6, upper=np.inf, default_value=10 + ), + "mass_thigh": UniformFloatContextFeature( + "mass_thigh", lower=1e-6, upper=np.inf, default_value=4.0578904 + ), + "mass_leg": UniformFloatContextFeature( + "mass_leg", lower=1e-6, upper=np.inf, default_value=2.7813568 + ), + "mass_foot": UniformFloatContextFeature( + "mass_foot", lower=1e-6, upper=np.inf, default_value=5.3155746 + ), + } diff --git a/carl/envs/brax/carl_humanoid.py b/carl/envs/brax/carl_humanoid.py index 873473ca..27a57146 100644 --- a/carl/envs/brax/carl_humanoid.py +++ b/carl/envs/brax/carl_humanoid.py @@ -1,113 +1,70 @@ -from typing import Any, Dict, List, Optional, Union +from __future__ import annotations -import copy -import json - -import brax import numpy as np -from brax import jumpy as jp -from brax.envs.humanoid import _SYSTEM_CONFIG, Humanoid -from brax.envs.wrappers import GymWrapper, VectorGymWrapper, VectorWrapper -from brax.physics import bodies -from google.protobuf import json_format, text_format -from google.protobuf.json_format import MessageToDict -from numpyencoder import NumpyEncoder - -from carl.context.selection import AbstractSelector -from carl.envs.carl_env import CARLEnv -from carl.utils.trial_logger import TrialLogger -from carl.utils.types import Context, Contexts - -DEFAULT_CONTEXT = { - "gravity": -9.8, - "friction": 0.6, - "angular_damping": -0.05, - "joint_angular_damping": 20, - "torso_mass": 8.907463, -} - -CONTEXT_BOUNDS = { - "gravity": (-np.inf, -0.1, float), - "friction": (-np.inf, np.inf, float), - "angular_damping": (-np.inf, np.inf, float), - "joint_angular_damping": (0, np.inf, float), - "torso_mass": (0.1, np.inf, float), -} - -class CARLHumanoid(CARLEnv): - def __init__( - self, - env: Humanoid = Humanoid(), - n_envs: int = 1, - contexts: Contexts = {}, - hide_context: bool = False, - add_gaussian_noise_to_context: bool = False, - gaussian_noise_std_percentage: float = 0.01, - logger: Optional[TrialLogger] = None, - scale_context_features: str = "no", - default_context: Optional[Context] = DEFAULT_CONTEXT, - state_context_features: Optional[List[str]] = None, - context_mask: Optional[List[str]] = None, - dict_observation_space: bool = False, - context_selector: Optional[ - Union[AbstractSelector, type[AbstractSelector]] - ] = None, - context_selector_kwargs: Optional[Dict] = None, - ): - if n_envs == 1: - env = GymWrapper(env) - else: - env = VectorGymWrapper(VectorWrapper(env, n_envs)) +from carl.context.context_space import ContextFeature, UniformFloatContextFeature +from carl.envs.brax.carl_brax_env import CARLBraxEnv - self.base_config = MessageToDict( - text_format.Parse(_SYSTEM_CONFIG, brax.Config()) - ) - if not contexts: - contexts = {0: DEFAULT_CONTEXT} - super().__init__( - env=env, - n_envs=n_envs, - contexts=contexts, - hide_context=hide_context, - add_gaussian_noise_to_context=add_gaussian_noise_to_context, - gaussian_noise_std_percentage=gaussian_noise_std_percentage, - logger=logger, - scale_context_features=scale_context_features, - default_context=default_context, - state_context_features=state_context_features, - dict_observation_space=dict_observation_space, - context_selector=context_selector, - context_selector_kwargs=context_selector_kwargs, - context_mask=context_mask, - ) - self.whitelist_gaussian_noise = list( - DEFAULT_CONTEXT.keys() - ) # allow to augment all values - def _update_context(self) -> None: - self.env: Humanoid - config = copy.deepcopy(self.base_config) - config["gravity"] = {"z": self.context["gravity"]} - config["friction"] = self.context["friction"] - config["angularDamping"] = self.context["angular_damping"] - for j in range(len(config["joints"])): - config["joints"][j]["angularDamping"] = self.context[ - "joint_angular_damping" - ] - config["bodies"][0]["mass"] = self.context["torso_mass"] - # This converts the dict to a JSON String, then parses it into an empty brax config - protobuf_config = json_format.Parse( - json.dumps(config, cls=NumpyEncoder), brax.Config() - ) - self.env.sys = brax.System(protobuf_config) - body = bodies.Body(config=self.env.sys.config) - body = jp.take(body, body.idx[:-1]) # skip the floor body - self.env.mass = body.mass.reshape(-1, 1) - self.env.inertia = body.inertia +class CARLBraxHumanoid(CARLBraxEnv): + env_name: str = "humanoid" + asset_path: str = "envs/assets/humanoid.xml" - def __getattr__(self, name: str) -> Any: - if name in ["sys", "body", "mass", "inertia"]: - return getattr(self.env._environment, name) - else: - return getattr(self, name) + @staticmethod + def get_context_features() -> dict[str, ContextFeature]: + return { + "gravity": UniformFloatContextFeature( + "gravity", lower=-1000, upper=-1e-6, default_value=-9.8 + ), + "friction": UniformFloatContextFeature( + "friction", lower=0, upper=100, default_value=1 + ), + "elasticity": UniformFloatContextFeature( + "elasticity", lower=0, upper=100, default_value=0 + ), + "ang_damping": UniformFloatContextFeature( + "ang_damping", lower=-np.inf, upper=np.inf, default_value=-0.05 + ), + "viscosity": UniformFloatContextFeature( + "viscosity", lower=0, upper=np.inf, default_value=0 + ), + "mass_torso": UniformFloatContextFeature( + "mass_torso", lower=1e-6, upper=np.inf, default_value=10 + ), + "mass_lwaist": UniformFloatContextFeature( + "mass_lwaist", lower=1e-6, upper=np.inf, default_value=2.2619467 + ), + "mass_pelvis": UniformFloatContextFeature( + "mass_pelvis", lower=1e-6, upper=np.inf, default_value=6.6161942 + ), + "mass_right_thigh": UniformFloatContextFeature( + "mass_right_thigh", lower=1e-6, upper=np.inf, default_value=4.751751 + ), + "mass_right_shin": UniformFloatContextFeature( + "mass_right_shin", lower=1e-6, upper=np.inf, default_value=4.522842 + ), + "mass_left_thigh": UniformFloatContextFeature( + "mass_left_thigh", lower=1e-6, upper=np.inf, default_value=4.751751 + ), + "mass_left_shin": UniformFloatContextFeature( + "mass_left_shin", lower=1e-6, upper=np.inf, default_value=4.522842 + ), + "mass_right_upper_arm": UniformFloatContextFeature( + "mass_right_upper_arm", + lower=1e-6, + upper=np.inf, + default_value=1.6610805, + ), + "mass_right_lower_arm": UniformFloatContextFeature( + "mass_right_lower_arm", + lower=1e-6, + upper=np.inf, + default_value=1.2295402, + ), + "mass_left_upper_arm": UniformFloatContextFeature( + "mass_left_upper_arm", lower=1e-6, upper=np.inf, default_value=1.6610805 + ), + "mass_left_lower_arm": UniformFloatContextFeature( + "mass_left_lower_arm", lower=1e-6, upper=np.inf, default_value=1.2295402 + ), + } diff --git a/carl/envs/brax/carl_humanoidstandup.py b/carl/envs/brax/carl_humanoidstandup.py new file mode 100644 index 00000000..7edb6ef6 --- /dev/null +++ b/carl/envs/brax/carl_humanoidstandup.py @@ -0,0 +1,70 @@ +from __future__ import annotations + +import numpy as np + +from carl.context.context_space import ContextFeature, UniformFloatContextFeature +from carl.envs.brax.carl_brax_env import CARLBraxEnv + + +class CARLBraxHumanoidStandup(CARLBraxEnv): + env_name: str = "humanoidstandup" + asset_path: str = "envs/assets/humanoidstandup.xml" + + @staticmethod + def get_context_features() -> dict[str, ContextFeature]: + return { + "gravity": UniformFloatContextFeature( + "gravity", lower=-1000, upper=-1e-6, default_value=-9.8 + ), + "friction": UniformFloatContextFeature( + "friction", lower=0, upper=100, default_value=1 + ), + "elasticity": UniformFloatContextFeature( + "elasticity", lower=0, upper=100, default_value=0 + ), + "ang_damping": UniformFloatContextFeature( + "ang_damping", lower=-np.inf, upper=np.inf, default_value=-0.05 + ), + "viscosity": UniformFloatContextFeature( + "viscosity", lower=0, upper=np.inf, default_value=0 + ), + "mass_torso": UniformFloatContextFeature( + "mass_torso", lower=1e-6, upper=np.inf, default_value=10 + ), + "mass_lwaist": UniformFloatContextFeature( + "mass_lwaist", lower=1e-6, upper=np.inf, default_value=2.2619467 + ), + "mass_pelvis": UniformFloatContextFeature( + "mass_pelvis", lower=1e-6, upper=np.inf, default_value=6.6161942 + ), + "mass_right_thigh": UniformFloatContextFeature( + "mass_right_thigh", lower=1e-6, upper=np.inf, default_value=4.751751 + ), + "mass_right_shin": UniformFloatContextFeature( + "mass_right_shin", lower=1e-6, upper=np.inf, default_value=4.522842 + ), + "mass_left_thigh": UniformFloatContextFeature( + "mass_left_thigh", lower=1e-6, upper=np.inf, default_value=4.751751 + ), + "mass_left_shin": UniformFloatContextFeature( + "mass_left_shin", lower=1e-6, upper=np.inf, default_value=4.522842 + ), + "mass_right_upper_arm": UniformFloatContextFeature( + "mass_right_upper_arm", + lower=1e-6, + upper=np.inf, + default_value=1.6610805, + ), + "mass_right_lower_arm": UniformFloatContextFeature( + "mass_right_lower_arm", + lower=1e-6, + upper=np.inf, + default_value=1.2295402, + ), + "mass_left_upper_arm": UniformFloatContextFeature( + "mass_left_upper_arm", lower=1e-6, upper=np.inf, default_value=1.6610805 + ), + "mass_left_lower_arm": UniformFloatContextFeature( + "mass_left_lower_arm", lower=1e-6, upper=np.inf, default_value=1.2295402 + ), + } diff --git a/carl/envs/brax/carl_inverted_double_pendulum.py b/carl/envs/brax/carl_inverted_double_pendulum.py new file mode 100644 index 00000000..07976e49 --- /dev/null +++ b/carl/envs/brax/carl_inverted_double_pendulum.py @@ -0,0 +1,40 @@ +from __future__ import annotations + +import numpy as np + +from carl.context.context_space import ContextFeature, UniformFloatContextFeature +from carl.envs.brax.carl_brax_env import CARLBraxEnv + + +class CARLBraxInvertedDoublePendulum(CARLBraxEnv): + env_name: str = "inverted_double_pendulum" + asset_path: str = "envs/assets/inverted_double_pendulum.xml" + + @staticmethod + def get_context_features() -> dict[str, ContextFeature]: + return { + "gravity": UniformFloatContextFeature( + "gravity", lower=-1000, upper=-1e-6, default_value=-9.8 + ), + "friction": UniformFloatContextFeature( + "friction", lower=0, upper=100, default_value=1 + ), + "elasticity": UniformFloatContextFeature( + "elasticity", lower=0, upper=100, default_value=0 + ), + "mass_cart": UniformFloatContextFeature( + "mass_cart", lower=1e-6, upper=np.inf, default_value=1 + ), + "mass_pole": UniformFloatContextFeature( + "mass_pole", lower=1e-6, upper=np.inf, default_value=1 + ), + "mass_pole2": UniformFloatContextFeature( + "mass_pole", lower=1e-6, upper=np.inf, default_value=1 + ), + "ang_damping": UniformFloatContextFeature( + "ang_damping", lower=-np.inf, upper=np.inf, default_value=-0.05 + ), + "viscosity": UniformFloatContextFeature( + "viscosity", lower=0, upper=np.inf, default_value=0 + ), + } diff --git a/carl/envs/brax/carl_inverted_pendulum.py b/carl/envs/brax/carl_inverted_pendulum.py new file mode 100644 index 00000000..280d81f5 --- /dev/null +++ b/carl/envs/brax/carl_inverted_pendulum.py @@ -0,0 +1,37 @@ +from __future__ import annotations + +import numpy as np + +from carl.context.context_space import ContextFeature, UniformFloatContextFeature +from carl.envs.brax.carl_brax_env import CARLBraxEnv + + +class CARLBraxInvertedPendulum(CARLBraxEnv): + env_name: str = "inverted_pendulum" + asset_path: str = "envs/assets/inverted_pendulum.xml" + + @staticmethod + def get_context_features() -> dict[str, ContextFeature]: + return { + "gravity": UniformFloatContextFeature( + "gravity", lower=-1000, upper=-1e-6, default_value=-9.8 + ), + "friction": UniformFloatContextFeature( + "friction", lower=0, upper=100, default_value=1 + ), + "elasticity": UniformFloatContextFeature( + "elasticity", lower=0, upper=100, default_value=0 + ), + "mass_cart": UniformFloatContextFeature( + "mass_cart", lower=1e-6, upper=np.inf, default_value=1 + ), + "mass_pole": UniformFloatContextFeature( + "mass_pole", lower=1e-6, upper=np.inf, default_value=1 + ), + "ang_damping": UniformFloatContextFeature( + "ang_damping", lower=-np.inf, upper=np.inf, default_value=-0.05 + ), + "viscosity": UniformFloatContextFeature( + "viscosity", lower=0, upper=np.inf, default_value=0 + ), + } diff --git a/carl/envs/brax/carl_pusher.py b/carl/envs/brax/carl_pusher.py new file mode 100644 index 00000000..d7de1599 --- /dev/null +++ b/carl/envs/brax/carl_pusher.py @@ -0,0 +1,79 @@ +from __future__ import annotations + +import numpy as np + +from carl.context.context_space import ContextFeature, UniformFloatContextFeature +from carl.envs.brax.carl_brax_env import CARLBraxEnv + + +class CARLBraxPusher(CARLBraxEnv): + env_name: str = "pusher" + asset_path: str = "envs/assets/pusher.xml" + + @staticmethod + def get_context_features() -> dict[str, ContextFeature]: + return { + # General physics + "gravity": UniformFloatContextFeature( + "gravity", lower=-1000, upper=-1e-6, default_value=-9.8 + ), + "friction": UniformFloatContextFeature( + "friction", lower=0, upper=100, default_value=1 + ), + "elasticity": UniformFloatContextFeature( + "elasticity", lower=0, upper=100, default_value=0 + ), + "ang_damping": UniformFloatContextFeature( + "ang_damping", lower=-np.inf, upper=np.inf, default_value=-0.05 + ), + "viscosity": UniformFloatContextFeature( + "viscosity", lower=0, upper=np.inf, default_value=0 + ), + # Masses of the pusher robot + "mass_r_shoulder_pan_link": UniformFloatContextFeature( + "mass_r_shoulder_pan_link", + lower=1e-6, + upper=np.inf, + default_value=7.2935214e00, + ), + "mass_r_shoulder_lift_link": UniformFloatContextFeature( + "mass_r_shoulder_lift_link", + lower=1e-6, + upper=np.inf, + default_value=np.pi, + ), + "mass_r_upper_arm_roll_link": UniformFloatContextFeature( + "mass_r_upper_arm_roll_link", + lower=1e-6, + upper=np.inf, + default_value=1.7140529, + ), + "mass_r_elbow_flex_link": UniformFloatContextFeature( + "mass_r_elbow_flex_link", + lower=1e-6, + upper=np.inf, + default_value=4.0715042e-01, + ), + "mass_r_forearm_roll_link": UniformFloatContextFeature( + "mass_r_forearm_roll_link", + lower=1e-6, + upper=np.inf, + default_value=9.2818356e-01, + ), + "mass_r_wrist_flex_link": UniformFloatContextFeature( + "mass_r_wrist_flex_link", + lower=1e-6, + upper=np.inf, + default_value=5.0265482e-03, + ), + "mass_r_wrist_roll_link": UniformFloatContextFeature( + "mass_r_wrist_roll_link", + lower=1e-6, + upper=np.inf, + default_value=1.8346901e-01, + ), + # Mass of the object to be pushed + "mass_object": UniformFloatContextFeature( + "mass_object", lower=1e-6, upper=np.inf, default_value=1.8325957e-03 + ), + } diff --git a/carl/envs/brax/carl_reacher.py b/carl/envs/brax/carl_reacher.py new file mode 100644 index 00000000..a6d75b62 --- /dev/null +++ b/carl/envs/brax/carl_reacher.py @@ -0,0 +1,37 @@ +from __future__ import annotations + +import numpy as np + +from carl.context.context_space import ContextFeature, UniformFloatContextFeature +from carl.envs.brax.carl_brax_env import CARLBraxEnv + + +class CARLBraxReacher(CARLBraxEnv): + env_name: str = "reacher" + asset_path: str = "envs/assets/reacher.xml" + + @staticmethod + def get_context_features() -> dict[str, ContextFeature]: + return { + "gravity": UniformFloatContextFeature( + "gravity", lower=-1000, upper=-1e-6, default_value=-9.8 + ), + "friction": UniformFloatContextFeature( + "friction", lower=0, upper=100, default_value=1 + ), + "elasticity": UniformFloatContextFeature( + "elasticity", lower=0, upper=100, default_value=0 + ), + "ang_damping": UniformFloatContextFeature( + "ang_damping", lower=-np.inf, upper=np.inf, default_value=-0.05 + ), + "viscosity": UniformFloatContextFeature( + "viscosity", lower=0, upper=np.inf, default_value=0 + ), + "mass_body0": UniformFloatContextFeature( + "mass_body0", lower=1e-6, upper=np.inf, default_value=0.03560472 + ), + "mass_body1": UniformFloatContextFeature( + "mass_body1", lower=1e-6, upper=np.inf, default_value=0.03979351 + ), + } diff --git a/carl/envs/brax/carl_ur5e.py b/carl/envs/brax/carl_ur5e.py deleted file mode 100644 index 02ebd518..00000000 --- a/carl/envs/brax/carl_ur5e.py +++ /dev/null @@ -1,127 +0,0 @@ -from typing import Any, Dict, List, Optional, Union - -import copy -import json - -import brax -import numpy as np -from brax.envs.ur5e import _SYSTEM_CONFIG, Ur5e -from brax.envs.wrappers import GymWrapper, VectorGymWrapper, VectorWrapper -from google.protobuf import json_format, text_format -from google.protobuf.json_format import MessageToDict -from numpyencoder import NumpyEncoder - -from carl.context.selection import AbstractSelector -from carl.envs.carl_env import CARLEnv -from carl.utils.trial_logger import TrialLogger -from carl.utils.types import Context, Contexts - -DEFAULT_CONTEXT = { - "joint_stiffness": 40000, - "gravity": -9.81, - "friction": 0.6, - "angular_damping": -0.05, - "actuator_strength": 100, - "joint_angular_damping": 50, - "target_radius": 0.02, - "target_distance": 0.5, - "torso_mass": 1.0, -} - -CONTEXT_BOUNDS = { - "joint_stiffness": (1, np.inf, float), - "gravity": (-np.inf, -0.1, float), - "friction": (-np.inf, np.inf, float), - "angular_damping": (-np.inf, np.inf, float), - "actuator_strength": (1, np.inf, float), - "joint_angular_damping": (0, 360, float), - "target_radius": (0.01, np.inf, float), - "target_distance": (0.01, np.inf, float), - "torso_mass": (0, np.inf, float), -} - - -class CARLUr5e(CARLEnv): - def __init__( - self, - env: Ur5e = Ur5e(), - n_envs: int = 1, - contexts: Contexts = {}, - hide_context: bool = False, - add_gaussian_noise_to_context: bool = False, - gaussian_noise_std_percentage: float = 0.01, - logger: Optional[TrialLogger] = None, - scale_context_features: str = "no", - default_context: Optional[Context] = DEFAULT_CONTEXT, - state_context_features: Optional[List[str]] = None, - context_mask: Optional[List[str]] = None, - dict_observation_space: bool = False, - context_selector: Optional[ - Union[AbstractSelector, type[AbstractSelector]] - ] = None, - context_selector_kwargs: Optional[Dict] = None, - ): - if n_envs == 1: - env = GymWrapper(env) - else: - env = VectorGymWrapper(VectorWrapper(env, n_envs)) - - self.base_config = MessageToDict( - text_format.Parse(_SYSTEM_CONFIG, brax.Config()) - ) - if not contexts: - contexts = {0: DEFAULT_CONTEXT} - super().__init__( - env=env, - n_envs=n_envs, - contexts=contexts, - hide_context=hide_context, - add_gaussian_noise_to_context=add_gaussian_noise_to_context, - gaussian_noise_std_percentage=gaussian_noise_std_percentage, - logger=logger, - scale_context_features=scale_context_features, - default_context=default_context, - state_context_features=state_context_features, - dict_observation_space=dict_observation_space, - context_selector=context_selector, - context_selector_kwargs=context_selector_kwargs, - context_mask=context_mask, - ) - self.whitelist_gaussian_noise = list( - DEFAULT_CONTEXT.keys() - ) # allow to augment all values - - def _update_context(self) -> None: - self.env: Ur5e - config = copy.deepcopy(self.base_config) - config["gravity"] = {"z": self.context["gravity"]} - config["friction"] = self.context["friction"] - config["angularDamping"] = self.context["angular_damping"] - for j in range(len(config["joints"])): - config["joints"][j]["angularDamping"] = self.context[ - "joint_angular_damping" - ] - config["joints"][j]["stiffness"] = self.context["joint_stiffness"] - for a in range(len(config["actuators"])): - config["actuators"][a]["strength"] = self.context["actuator_strength"] - config["bodies"][0]["mass"] = self.context["torso_mass"] - # This converts the dict to a JSON String, then parses it into an empty brax config - self.env.sys = brax.System( - json_format.Parse(json.dumps(config, cls=NumpyEncoder), brax.Config()) - ) - self.env.target_idx = self.env.sys.body.index["Target"] - self.env.torso_idx = self.env.sys.body.index["wrist_3_link"] - self.env.target_radius = self.context["target_radius"] - self.env.target_distance = self.context["target_distance"] - - def __getattr__(self, name: str) -> Any: - if name in [ - "sys", - "target_idx", - "torso_idx", - "target_radius", - "target_distance", - ]: - return getattr(self.env._environment, name) - else: - return getattr(self, name) diff --git a/carl/envs/brax/carl_walker2d.py b/carl/envs/brax/carl_walker2d.py new file mode 100644 index 00000000..3aa66b89 --- /dev/null +++ b/carl/envs/brax/carl_walker2d.py @@ -0,0 +1,52 @@ +from __future__ import annotations + +import numpy as np + +from carl.context.context_space import ContextFeature, UniformFloatContextFeature +from carl.envs.brax.carl_brax_env import CARLBraxEnv + + +class CARLBraxWalker2d(CARLBraxEnv): + env_name: str = "walker2d" + asset_path: str = "envs/assets/walker2d.xml" + + @staticmethod + def get_context_features() -> dict[str, ContextFeature]: + return { + "gravity": UniformFloatContextFeature( + "gravity", lower=-1000, upper=-1e-6, default_value=-9.8 + ), + "friction": UniformFloatContextFeature( + "friction", lower=0, upper=100, default_value=1 + ), + "elasticity": UniformFloatContextFeature( + "elasticity", lower=0, upper=100, default_value=0 + ), + "ang_damping": UniformFloatContextFeature( + "ang_damping", lower=-np.inf, upper=np.inf, default_value=-0.05 + ), + "viscosity": UniformFloatContextFeature( + "viscosity", lower=0, upper=np.inf, default_value=0 + ), + "mass_torso": UniformFloatContextFeature( + "mass_torso", lower=1e-6, upper=np.inf, default_value=10 + ), + "mass_thigh": UniformFloatContextFeature( + "mass_thigh", lower=1e-6, upper=np.inf, default_value=4.0578904 + ), + "mass_leg": UniformFloatContextFeature( + "mass_leg", lower=1e-6, upper=np.inf, default_value=2.7813568 + ), + "mass_foot": UniformFloatContextFeature( + "mass_foot", lower=1e-6, upper=np.inf, default_value=3.1667254 + ), + "mass_thigh_left": UniformFloatContextFeature( + "mass_thigh_left", lower=1e-6, upper=np.inf, default_value=4.0578904 + ), + "mass_leg_left": UniformFloatContextFeature( + "mass_leg_left", lower=1e-6, upper=np.inf, default_value=2.7813568 + ), + "mass_foot_left": UniformFloatContextFeature( + "mass_foot_left", lower=1e-6, upper=np.inf, default_value=3.1667254 + ), + } diff --git a/carl/envs/brax/wrappers.py b/carl/envs/brax/wrappers.py new file mode 100644 index 00000000..bdea842b --- /dev/null +++ b/carl/envs/brax/wrappers.py @@ -0,0 +1,166 @@ +# Updated version of the brax gym wrappers to fix rendering issues. +# Hopefully we can go back to the original version with v1 of brax. +# +# The original copyright: +# Copyright 2023 The Brax Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Wrappers to convert brax envs to gym envs.""" +from typing import ClassVar, Optional + +import gym +import jax +import numpy as np +from brax.envs.base import PipelineEnv +from brax.io import image +from gym import spaces +from gym.vector import utils + + +class GymWrapper(gym.Env): + """A wrapper that converts Brax Env to one that follows Gym API.""" + + # Flag that prevents `gym.register` from misinterpreting the `_step` and + # `_reset` as signs of a deprecated gym Env API. + _gym_disable_underscore_compat: ClassVar[bool] = True + + def __init__( + self, + env: PipelineEnv, + seed: int = 0, + backend: Optional[str] = None, + render_mode: str = "rgb_array", + ): + self._env = env + self.metadata = { + "render.modes": ["human", "rgb_array"], + "video.frames_per_second": 1 / self._env.dt, + } + self.seed(seed) + self.backend = backend + self._state = None + self.render_mode = render_mode + + obs = np.inf * np.ones(self._env.observation_size, dtype="float32") + self.observation_space = spaces.Box(-obs, obs, dtype="float32") + + action = np.ones(self._env.action_size, dtype="float32") + self.action_space = spaces.Box(-action, action, dtype="float32") + + def reset(key): + key1, key2 = jax.random.split(key) + state = self._env.reset(key2) + return state, state.obs, key1 + + self._reset = jax.jit(reset, backend=self.backend) + + def step(state, action): + state = self._env.step(state, action) + info = {**state.metrics, **state.info} + return state, state.obs, state.reward, state.done, info + + self._step = jax.jit(step, backend=self.backend) + + def reset(self, seed: Optional[int] = None, options: dict = {}): + self._state, obs, self._key = self._reset(self._key) + # We return device arrays for pytorch users. + return obs, {} + + def step(self, action): + self._state, obs, reward, done, info = self._step(self._state, action) + # We return device arrays for pytorch users. + return obs, reward, done, False, info + + def seed(self, seed: int = 0): + self._key = jax.random.PRNGKey(seed) + + def render(self): + if self.render_mode == "rgb_array": + sys, state = self._env.sys, self._state + if state is None: + raise RuntimeError("must call reset or step before rendering") + return image.render_array(sys, state.pipeline_state, 256, 256) + else: + return super().render() # just raise an exception + + +class VectorGymWrapper(gym.vector.VectorEnv): + """A wrapper that converts batched Brax Env to one that follows Gym VectorEnv API.""" + + # Flag that prevents `gym.register` from misinterpreting the `_step` and + # `_reset` as signs of a deprecated gym Env API. + _gym_disable_underscore_compat: ClassVar[bool] = True + + def __init__( + self, + env: PipelineEnv, + seed: int = 0, + backend: Optional[str] = None, + render_mode="rgb_array", + ): + self._env = env + self.metadata = { + "render.modes": ["human", "rgb_array"], + "video.frames_per_second": 1 / self._env.dt, + } + self.render_mode = render_mode + if not hasattr(self._env, "batch_size"): + raise ValueError("underlying env must be batched") + + self.num_envs = self._env.batch_size + self.seed(seed) + self.backend = backend + self._state = None + + obs = np.inf * np.ones(self._env.observation_size, dtype="float32") + obs_space = spaces.Box(-obs, obs, dtype="float32") + self.observation_space = utils.batch_space(obs_space, self.num_envs) + + action = np.ones(self._env.action_size, dtype="float32") + action_space = spaces.Box(-action, action, dtype="float32") + self.action_space = utils.batch_space(action_space, self.num_envs) + + def reset(key): + key1, key2 = jax.random.split(key) + state = self._env.reset(key2) + return state, state.obs, key1 + + self._reset = jax.jit(reset, backend=self.backend) + + def step(state, action): + state = self._env.step(state, action) + info = {**state.metrics, **state.info} + return state, state.obs, state.reward, state.done, info + + self._step = jax.jit(step, backend=self.backend) + + def reset(self): + self._state, obs, self._key = self._reset(self._key) + return obs, {} + + def step(self, action): + self._state, obs, reward, done, info = self._step(self._state, action) + return obs, reward, done, False, info + + def seed(self, seed: int = 0): + self._key = jax.random.PRNGKey(seed) + + def render(self): + if self.render_mode == "rgb_array": + sys, state = self._env.sys, self._state + if state is None: + raise RuntimeError("must call reset or step before rendering") + return image.render_array(sys, state.pipeline_state, 256, 256) + else: + return super().render() # just raise an exception diff --git a/carl/envs/carl_env.py b/carl/envs/carl_env.py index e0f094c1..00105c88 100644 --- a/carl/envs/carl_env.py +++ b/carl/envs/carl_env.py @@ -1,126 +1,94 @@ from __future__ import annotations -from typing import Any, Dict, List, Mapping, Optional, Tuple, Type, Union +import abc +from typing import Any, SupportsFloat, TypeVar -import importlib import inspect -import json -import os -from types import ModuleType -import gym -import numpy as np -from gym import Wrapper, spaces +import gymnasium +from gymnasium import Wrapper, spaces +from gymnasium.core import Env -from carl.context.augmentation import add_gaussian_noise +from carl.context.context_space import ContextFeature, ContextSpace from carl.context.selection import AbstractSelector, RoundRobinSelector -from carl.context.utils import get_context_bounds -from carl.utils.trial_logger import TrialLogger -from carl.utils.types import Context, Contexts, ObsType, Vector - -brax_spec = importlib.util.find_spec("brax") -if brax_spec is not None: - import jax.numpy as jnp - import jaxlib - - -class CARLEnv(Wrapper): - """ - Meta-environment formulating the original environments as cMDPs. - - Here, a context feature can be anything defining the behavior of the - environment. An instance is the environment with a specific context. - - Can change the context after each episode. - - If not all keys are present in the provided context(s) the contexts will be filled - with the default context values in the init of the class. - - Parameters - ---------- - env: gym.Env - Environment which context features are made visible / which is turned into a cMDP. - contexts: Contexts - Dict of contexts/instances. Key are context id, values are contexts as - Dict[context feature id, context feature value]. - hide_context: bool = False - If False, the context will be appended to the original environment's state. - add_gaussian_noise_to_context: bool = False - Wether to add Gaussian noise to the context with the relative standard deviation - 'gaussian_noise_std_percentage'. - gaussian_noise_std_percentage: float = 0.01 - The relative standard deviation for the Gaussian noise. The actual standard deviation - is calculated by 'gaussian_noise_std_percentage' * context feature value. - logger: TrialLogger, optional - Optional TrialLogger which takes care of setting up logging directories and handles - custom logging. - max_episode_length: int = 1e6 - Maximum length of episode in (time)steps. Cutoff. - scale_context_features: str = "no" - Wether to scale context features. Available modes are 'no', 'by_mean' and 'by_default'. - 'by_mean' scales the context features by their mean over all passed instances and - 'by_default' scales the context features by their default values ('default_context'). - default_context: Context - The default context of the environment. Used for scaling the context features if applicable. Used for filling - incomplete contexts. - state_context_features: Optional[List[str]] = None - If the context is visible to the agent (hide_context=False), the context features are appended to the state. - state_context_features specifies which of the context features are appended to the state. The default is - appending all context features. - context_mask: Optional[List[str]] - Name of context features to be ignored when appending context features to the state. - context_selector: Optional[Union[AbstractSelector, type(AbstractSelector)]] - Context selector (object of) class, e.g., can be RoundRobinSelector (default) or RandomSelector. - Should subclass AbstractSelector. - context_selector_kwargs: Optional[Dict] - Optional kwargs for context selector class. - - Raises - ------ - ValueError - If the choice of instance_mode is not available. - ValueError - If the choice of scale_context_features is not available. - - """ - - available_scale_methods = ["by_default", "by_mean", "no"] - available_instance_modes = ["random", "rr", "roundrobin"] +from carl.utils.types import Context, Contexts +ObsType = TypeVar("ObsType") + + +class CARLEnv(Wrapper, abc.ABC): def __init__( self, - env: gym.Env, - n_envs: int = 1, - contexts: Contexts = {}, - hide_context: bool = True, - add_gaussian_noise_to_context: bool = False, - gaussian_noise_std_percentage: float = 0.01, - logger: Optional[TrialLogger] = None, - max_episode_length: int = int(1e6), - scale_context_features: str = "no", - default_context: Optional[Context] = None, - state_context_features: Optional[List[str]] = None, - context_mask: Optional[List[str]] = None, - dict_observation_space: bool = False, - context_selector: Optional[ - Union[AbstractSelector, Type[AbstractSelector]] - ] = None, - context_selector_kwargs: Optional[Dict] = None, + env: Env, + contexts: Contexts | None = None, + obs_context_features: list[str] | None = None, + obs_context_as_dict: bool = True, + context_selector: AbstractSelector | type[AbstractSelector] | None = None, + context_selector_kwargs: dict | None = None, + **kwargs, ): - super().__init__(env=env) - # Gather args - self._context: Context # init for property - self._contexts: Contexts # init for property - self.default_context = default_context + """Base CARL wrapper. + + Good to know: + + - The observation always is a dictionary of {"state": ..., "context": ...}. Use + an observation flattening wrapper if you need a different format. + - After each env reset, a new context is selected by the context selector. + - The context set is always filled with defaults if missing. + + Parameters + ---------- + env : Env + Environment adhering to gymnasium API. + contexts : Contexts, optional + The context set, by default None. + obs_context_features : list[str], optional + The context features which should be added to the state, by default None. If None, + add all available context features. + obs_context_as_dict : bool, optional + Whether to pass the context as a vector or a dict in the observations. + The default is True. + context_selector : AbstractSelector | type[AbstractSelector] | None + The context selector selecting a new context after each env reset, by default None. + If None, use a round robin selector. Can be an object or class. For the latter, + you can pass kwargs. + context_selector_kwargs : dict, optional + Keyword arguments for the context selector if it is passed as a class. + + + Attributes + ---------- + base_observation_space: gymnasium.spaces.Space + The observation space from the wrapped environment. + obs_context_as_dict: bool, optional + Whether to pass the context as a vector or a dict in the observations. + The default is True. + observation_space: gymnasium.spaces.Dict + The observation space of the CARL environment which is a dictionary of + "state" and "context". + contexts: Contexts + The context set. + context_selector: ContextSelector. + The context selector selecting a new context after each env reset. + + """ + super().__init__(env) + + self.base_observation_space: gymnasium.spaces.Space = env.observation_space + self.obs_context_as_dict = obs_context_as_dict + + if contexts is None: + contexts = { + 0: self.get_default_context() + } # was self.get_default_context(self) before self.contexts = contexts - self.context_mask = context_mask - self.hide_context = hide_context - self.dict_observation_space = dict_observation_space - self.cutoff = max_episode_length - self.logger = logger - self.add_gaussian_noise_to_context = add_gaussian_noise_to_context - self.gaussian_noise_std_percentage = gaussian_noise_std_percentage - self.context_selector: Type[AbstractSelector] + self.context: Context | None = None # Set by `_progress_instance` + if obs_context_features is None: + obs_context_features = list(list(self.contexts.values())[0].keys()) + self.obs_context_features = obs_context_features + + # Context Selector + self.context_selector: type[AbstractSelector] if context_selector is None: self.context_selector = RoundRobinSelector(contexts=contexts) # type: ignore [assignment] elif isinstance(context_selector, AbstractSelector): @@ -138,270 +106,124 @@ def __init__( f"Context selector must be None or an AbstractSelector class or instance. " f"Got type {type(context_selector)}." ) - context_keys: Vector - if state_context_features is not None: - if state_context_features == "changing_context_features" or ( - type(state_context_features) == list - and state_context_features[0] == "changing_context_features" - ): - # if we have only one context the context features do not change during training - if len(self.contexts) > 1: - # detect which context feature changes - context_array = np.array( - [np.array(list(c.values())) for c in self.contexts.values()] - ) - which_cf_changes = ~np.all( - context_array == context_array[0, :], axis=0 - ) - context_keys = np.array( - list(self.contexts[list(self.contexts.keys())[0]].keys()) - ) - state_context_features = context_keys[which_cf_changes] - # TODO properly record which are appended to state - if logger is not None: - fname = os.path.join(logger.logdir, "env_info.json") - save_val: Optional[List[str]] - if state_context_features is not None: - save_val = list(state_context_features) # please json - else: - save_val = state_context_features - with open(fname, "w") as file: - data = {"state_context_features": save_val} - json.dump(data, file, indent="\t") - else: - state_context_features = [] - else: - state_context_features = list( - self.contexts[list(self.contexts.keys())[0]].keys() - ) - self.state_context_features: List[str] = state_context_features # type: ignore [assignment] - # (Mypy thinks that state_context_features is of type Optional[List[str]] which it can't be anymore due to the - # if-else clause) - - # state_context_features contains the names of the context features that should be appended to the state - # However, if context_mask is set, we want to update state_context_feature_names so that the context features - # in context_mask are not appended to the state anymore. - if self.context_mask: - self.state_context_features = [ - s for s in self.state_context_features if s not in self.context_mask - ] - - self.step_counter = 0 # type: int # increased in/after step - self.total_timestep_counter = 0 # type: int - self.episode_counter = -1 # type: int # increased during reset - self.whitelist_gaussian_noise = ( - None - ) # type: Optional[List[str]] # holds names of context features - # where it is allowed to add gaussian noise - - # Set initial context - # TODO only set context during reset? - # Don't use the context selector. This way after the first reset we actually - # start with the first context. We just need a default/initial context here - # so all the tests and the rest does not break. - context_keys = list(self.contexts.keys()) - self.context = self.contexts[context_keys[0]] - - # Scale context features - if scale_context_features not in self.available_scale_methods: - raise ValueError( - f"{scale_context_features} not in {self.available_scale_methods}." - ) - self.scale_context_features = scale_context_features - self.context_feature_scale_factors = None - if self.scale_context_features == "by_mean": - cfs_vals = np.concatenate( - [np.array(list(v.values()))[:, None] for v in self.contexts.values()], - axis=-1, - ) - self.context_feature_scale_factors = np.mean(cfs_vals, axis=-1) - self.context_feature_scale_factors[ - self.context_feature_scale_factors == 0 - ] = 1 # otherwise value / scale_factor = nan - elif self.scale_context_features == "by_default": - if self.default_context is None: - raise ValueError( - "Please set default_context for scale_context_features='by_default'." - ) - self.context_feature_scale_factors = np.array( - list(self.default_context.values()) - ) - self.context_feature_scale_factors[ - self.context_feature_scale_factors == 0 - ] = 1 # otherwise value / scale_factor = nan - - self.vectorized = n_envs > 1 - self.build_observation_space() - @property - def context(self) -> Dict: - return self._context - - @context.setter - def context(self, context: Context) -> None: - self._context = self.fill_context_with_default(context=context) + self.observation_space: gymnasium.spaces.Dict = self.get_observation_space( + obs_context_feature_names=self.obs_context_features + ) @property - def context_key(self) -> Any | None: - return self.context_selector.context_key + def contexts(self) -> Contexts: + return self._contexts @property - def contexts(self) -> Dict[Any, Dict[Any, Any]]: - return self._contexts + def context_id(self): + return self.context_selector.context_id @contexts.setter def contexts(self, contexts: Contexts) -> None: - self._contexts = { - k: self.fill_context_with_default(context=v) for k, v in contexts.items() - } + """Set `contexts` property - def reset(self, **kwargs: Dict) -> Union[ObsType, tuple[ObsType, dict]]: # type: ignore [override] - """ - Reset environment. + For each context maybe fill with default context values. + This is only necessary whenever we update the contexts, + so here is the right place. Parameters ---------- - kwargs: Dict - Any keyword arguments passed to env.reset(). + contexts : Contexts + Contexts to set + """ + context_space = self.get_context_space() + contexts = {k: context_space.insert_defaults(v) for k, v in contexts.items()} + self._contexts = contexts - Returns - ------- - state - State of environment after reset. - info_dict : dict - Return also if return_info=True. + @context_id.setter + def context_id(self, new_id) -> None: + """Set `context_id` property + This will switch the context ID of the context selector. + Realistically you'll want to only use this if your selector does not automaticall progress the instances. + + Parameters + ---------- + new_id : + ID to set the context to """ - self.episode_counter += 1 - self.step_counter = 0 - self._progress_instance() + assert ( + new_id in self.context_selector.context_ids + ), "Unknown ID, this context does not exist in the context set." + self.context_selector.context_id = new_id + self.context_selector.context = self.context_selector.contexts[new_id] + self.context = self.context_selector.context self._update_context() - self._log_context() - return_info = kwargs.get("return_info", False) - _ret = self.env.reset(**kwargs) # type: ignore [arg-type] - info_dict = dict() - if return_info: - state, info_dict = _ret - else: - state = _ret - state = self.build_context_adaptive_state(state=state) - ret = state - if return_info: - ret = state, info_dict - return ret - - def build_context_adaptive_state( - self, state: List[float], context_feature_values: Optional[Vector] = None - ) -> Union[Vector, Dict]: - tnp: ModuleType = np - if brax_spec is not None: - if type(state) == jaxlib.xla_extension.DeviceArray: - tnp = jnp - if not self.hide_context: - if context_feature_values is None: - # use current context - context_values = tnp.array(list(self.context.values())) - else: - # use potentially modified context - context_values = context_feature_values - # Append context to state - if self.state_context_features is not None: - # if self.state_context_features is an empty list, the context values will also be empty and we - # get the correct state - context_keys = list(self.context.keys()) - context_values = tnp.array( - [ - context_values[context_keys.index(k)] - for k in self.state_context_features - ] - ) - - if self.dict_observation_space: - state: Dict = dict(state=state, context=context_values) # type: ignore [no-redef] - elif self.vectorized: - state = tnp.array([np.concatenate((s, context_values)) for s in state]) - else: - state = tnp.concatenate((state, context_values)) - return state - - def step(self, action: Any) -> Tuple[Any, Any, bool, Dict]: - """ - Step the environment. - - 1. Step - 2. Add (potentially scaled) context features to state if hide_context = False. - Emits done if the environment has taken more steps than cutoff (max_episode_length). + def get_observation_space( + self, obs_context_feature_names: list[str] | None = None + ) -> gymnasium.spaces.Dict: + """Get the observation space for the context. Parameters ---------- - action: - Action to pass to env.step. + obs_context_feature_names : list[str] | None, optional + Name of the context features to be included in the observation, by default None. + If it is None, we add all context features. Returns ------- - state, reward, done, info: Any, Any, bool, Dict - Standard signature. - + gymnasium.spaces.Dict + Gymnasium observation space which contains the observation space of the + underlying environment ("state") and for the context ("context"). """ - # Step the environment - state, reward, done, info = self.env.step(action) - - if not self.hide_context: - # Scale context features - context_feature_values = np.array(list(self.context.values())) - if self.scale_context_features == "by_default": - context_feature_values /= self.context_feature_scale_factors - elif self.scale_context_features == "by_mean": - context_feature_values /= self.context_feature_scale_factors - elif self.scale_context_features == "no": - pass - else: - raise ValueError( - f"{self.scale_context_features} not in {self.available_scale_methods}." - ) - - # Add context features to state - state = self.build_context_adaptive_state( - state=state, context_feature_values=context_feature_values - ) + context_space = self.get_context_space() + obs_space_context = context_space.to_gymnasium_space( + context_feature_names=obs_context_feature_names, + as_dict=self.obs_context_as_dict, + ) - self.total_timestep_counter += 1 - self.step_counter += 1 - if self.step_counter >= self.cutoff: - done = True - return state, reward, done, info - - def __getattr__(self, name: str) -> Any: - # TODO: does this work with activated noise? I think we need to update it - # We need this because our CARLEnv has underscore class methods which would - # throw an error otherwise - if name in ["_progress_instance", "_update_context", "_log_context"]: - return getattr(self, name) - if name.startswith("_"): - raise AttributeError( - "attempted to get missing private attribute '{}'".format(name) - ) - return getattr(self.env, name) + obs_space = spaces.Dict( + { + "obs": self.base_observation_space, + "context": obs_space_context, + } + ) + return obs_space + + @staticmethod + @abc.abstractmethod + def get_context_features() -> dict[str, ContextFeature]: + """Get the context features - def fill_context_with_default(self, context: Context) -> Dict: + Defined per environment. + + Returns + ------- + dict[str, ContextFeature] + Context feature definitions """ - Fill the context with the default values if entries are missing + ... - Parameters - ---------- - context + @classmethod + def get_context_space(cls) -> ContextSpace: + """Get context space Returns ------- - context + ContextSpace + Context space with utility methods holding + information about defaults, types, bounds, etc. + """ + return ContextSpace(cls.get_context_features()) + + @classmethod + def get_default_context(cls) -> Context: + """Get the default context + Returns + ------- + Context + Default context. """ - if self.default_context: - context_def = self.default_context.copy() - context_def.update(context) - context = context_def - return context + default_context = cls.get_context_space().get_default_context() + return default_context def _progress_instance(self) -> None: """ @@ -411,7 +233,6 @@ def _progress_instance(self) -> None: 1. Select instance with the instance_mode. If the instance_mode is random, randomly select the next instance from the set of contexts. If instance_mode is rr or roundrobin, select the next instance. - 2. If Gaussian noise should be added to whitelisted context features, do so. Returns ------- @@ -419,159 +240,103 @@ def _progress_instance(self) -> None: """ context = self.context_selector.select() # type: ignore [call-arg] - - if self.add_gaussian_noise_to_context and self.whitelist_gaussian_noise: - context_augmented = {} - for key, value in context.items(): - if key in self.whitelist_gaussian_noise: - context_augmented[key] = add_gaussian_noise( - default_value=value, - percentage_std=self.gaussian_noise_std_percentage, - random_generator=None, # self.np_random TODO discuss this - ) - else: - context_augmented[key] = context[key] - context = context_augmented self.context = context - def build_observation_space( - self, - env_lower_bounds: Optional[Vector] = None, - env_upper_bounds: Optional[Vector] = None, - context_bounds: Optional[Mapping[str, Tuple[float, float, type]]] = None, - ) -> None: - """ - Build observation space of environment. + def reset( + self, *, seed: int | None = None, options: dict[str, Any] | None = None + ) -> tuple[Any, dict[str, Any]]: + """Reset the environment. - If the hide_context = False, add correct bounds for the context features to the - observation space. + First, we progress the instance, i.e. select a new context with the context + selector. Then we update the context in the wrapped environment. + Finally, we reset the underlying environment and add context information + to the observation. Parameters ---------- - env_lower_bounds: Optional[Union[List, np.array]], default=None - Lower bounds for environment observation space. If env_lower_bounds and env_upper_bounds - both are None, (re-)create bounds (low=-inf, high=inf) with correct dimension. - env_upper_bounds: Optional[Union[List, np.array]], default=None - Upper bounds for environment observation space. - context_bounds: Optional[Dict[str, Tuple[float, float, float]]], default=None - Lower and upper bounds for context features. - The bounds are provided as a Dict containing the context feature names/ids as keys and the - bounds per feature as a tuple (low, high, dtype). - If None and the context should not be hidden, - creates default bounds with (low=-inf, high=inf) with correct dimension. - - Raises - ------ - ValueError: - If (env.)observation space is not gym.spaces.Box and the context should not be hidden - (hide_context = False). + seed : int | None, optional + Seed, by default None + options : dict[str, Any] | None, optional + Options, by default None Returns ------- - None + tuple[Any, dict[str, Any]] + Observation, info. + """ + last_context_id = self.context_id + self._progress_instance() + if self.context_id != last_context_id: + self._update_context() + state, info = super().reset(seed=seed, options=options) + state = self._add_context_to_state(state) + info["context_id"] = self.context_id + return state, info + + def _add_context_to_state(self, state: Any) -> dict[str, Any]: + """Add context observation to the state + + The state is the observation from the underlying environment + and we add the context information to it. We return a dictionary + of the state and context, and the context is maybe represented + as a dictionary itself (controlled via `self.obs_context_as_dict`). + Parameters + ---------- + state : Any + State from the environment + + Returns + ------- + dict[str, Any] + State context observation dict """ - self.observation_space: gym.spaces.Space - if ( - not self.dict_observation_space - and not isinstance(self.observation_space, spaces.Box) - and not self.hide_context - ): - raise ValueError( - "This environment does not yet support non-hidden contexts. Only supports " - "Box observation spaces." - ) - obs_space = ( - self.env.observation_space.spaces["state"].low - if isinstance(self.env.observation_space, spaces.Dict) - else self.env.observation_space.low # type: ignore [attr-defined] - ) - obs_shape = obs_space.shape - if len(obs_shape) == 3 and self.hide_context: - # do not touch pixel state - pass + if not self.obs_context_as_dict: + context = [self.context[k] for k in self.obs_context_features] else: - if env_lower_bounds is None and env_upper_bounds is None: - obs_dim = obs_shape[0] - env_lower_bounds = -np.inf * np.ones(obs_dim) - env_upper_bounds = np.inf * np.ones(obs_dim) - - if self.hide_context or ( - self.state_context_features is not None - and len(self.state_context_features) == 0 - ): - self.env.observation_space = spaces.Box( - np.array(env_lower_bounds), - np.array(env_upper_bounds), - dtype=np.float32, - ) - else: - context_keys = list(self.context.keys()) - if context_bounds is None: - context_dim = len(list(self.context.keys())) - context_lower_bounds = -np.inf * np.ones(context_dim) - context_upper_bounds = np.inf * np.ones(context_dim) - else: - context_lower_bounds, context_upper_bounds = get_context_bounds( - context_keys, context_bounds # type: ignore [arg-type] - ) - if self.state_context_features is not None: - ids = np.array( - [context_keys.index(k) for k in self.state_context_features] - ) - context_lower_bounds = context_lower_bounds[ids] - context_upper_bounds = context_upper_bounds[ids] - if self.dict_observation_space: - self.env.observation_space = spaces.Dict( - { - "state": spaces.Box( - low=np.array(env_lower_bounds), - high=np.array(env_upper_bounds), - dtype=np.float32, - ), - "context": spaces.Box( - low=np.array(context_lower_bounds), - high=np.array(context_upper_bounds), - dtype=np.float32, - ), - } - ) - else: - low: Vector = np.concatenate( - (np.array(env_lower_bounds), np.array(context_lower_bounds)) - ) - high: Vector = np.concatenate( - (np.array(env_upper_bounds), np.array(context_upper_bounds)) - ) - self.env.observation_space = spaces.Box( - low=np.array(low), high=np.array(high), dtype=np.float32 - ) - self.observation_space = ( - self.env.observation_space - ) # make sure it is the same object + context = { + k: v for k, v in self.context.items() if k in self.obs_context_features + } + state_context_dict = { + "obs": state, + "context": context, + } + return state_context_dict + @abc.abstractmethod def _update_context(self) -> None: """ Update the context feature values of the environment. + `self._progress_instance` must be called at least once to se(lec)t a valid context. + Returns ------- None """ - raise NotImplementedError + ... - def _log_context(self) -> None: - """ - Log context. + def step( + self, action: Any + ) -> tuple[Any, SupportsFloat, bool, bool, dict[str, Any]]: + """Step the environment. + + The context is added to the observation returned by the + wrapped environment. + + Parameters + ---------- + action : Any + Action Returns ------- - None - + tuple[Any, SupportsFloat, bool, bool, dict[str, Any]] + Observation, rewar, terminated, truncated, info. """ - if self.logger: - self.logger.write_context( - self.episode_counter, self.total_timestep_counter, self.context - ) + state, reward, terminated, truncated, info = super().step(action) + state = self._add_context_to_state(state) + info["context_id"] = self.context_id + return state, reward, terminated, truncated, info diff --git a/carl/envs/classic_control/__init__.py b/carl/envs/classic_control/__init__.py deleted file mode 100644 index 1acb7d10..00000000 --- a/carl/envs/classic_control/__init__.py +++ /dev/null @@ -1,39 +0,0 @@ -# flake8: noqa: F401 -# Contexts and bounds by name -from carl.envs.classic_control.carl_acrobot import ( - CONTEXT_BOUNDS as CARLAcrobotEnv_bounds, -) -from carl.envs.classic_control.carl_acrobot import ( - DEFAULT_CONTEXT as CARLAcrobotEnv_defaults, -) -from carl.envs.classic_control.carl_acrobot import CARLAcrobotEnv -from carl.envs.classic_control.carl_cartpole import ( - CONTEXT_BOUNDS as CARLCartPoleEnv_bounds, -) -from carl.envs.classic_control.carl_cartpole import ( - DEFAULT_CONTEXT as CARLCartPoleEnv_defaults, -) -from carl.envs.classic_control.carl_cartpole import CARLCartPoleEnv -from carl.envs.classic_control.carl_mountaincar import ( - CONTEXT_BOUNDS as CARLMountainCarEnv_bounds, -) -from carl.envs.classic_control.carl_mountaincar import ( - DEFAULT_CONTEXT as CARLMountainCarEnv_defaults, -) -from carl.envs.classic_control.carl_mountaincar import CARLMountainCarEnv -from carl.envs.classic_control.carl_mountaincarcontinuous import ( - CONTEXT_BOUNDS as CARLMountainCarContinuousEnv_bounds, -) -from carl.envs.classic_control.carl_mountaincarcontinuous import ( - DEFAULT_CONTEXT as CARLMountainCarContinuousEnv_defaults, -) -from carl.envs.classic_control.carl_mountaincarcontinuous import ( - CARLMountainCarContinuousEnv, -) -from carl.envs.classic_control.carl_pendulum import ( - CONTEXT_BOUNDS as CARLPendulumEnv_bounds, -) -from carl.envs.classic_control.carl_pendulum import ( - DEFAULT_CONTEXT as CARLPendulumEnv_defaults, -) -from carl.envs.classic_control.carl_pendulum import CARLPendulumEnv diff --git a/carl/envs/classic_control/carl_acrobot.py b/carl/envs/classic_control/carl_acrobot.py deleted file mode 100644 index a3c9aea0..00000000 --- a/carl/envs/classic_control/carl_acrobot.py +++ /dev/null @@ -1,163 +0,0 @@ -from typing import Dict, List, Optional, Union - -import numpy as np -from gym.envs.classic_control import AcrobotEnv - -from carl.context.selection import AbstractSelector -from carl.envs.carl_env import CARLEnv -from carl.utils.trial_logger import TrialLogger -from carl.utils.types import Context, Contexts - -DEFAULT_CONTEXT = { - "link_length_1": 1, # should be seen as 100% default and scaled - "link_length_2": 1, # should be seen as 100% default and scaled - "link_mass_1": 1, # should be seen as 100% default and scaled - "link_mass_2": 1, # should be seen as 100% default and scaled - "link_com_1": 0.5, # Percentage of the length of link one - "link_com_2": 0.5, # Percentage of the length of link one - "link_moi": 1, # should be seen as 100% default and scaled - "max_velocity_1": 4 * np.pi, - "max_velocity_2": 9 * np.pi, - "torque_noise_max": 0.0, # optional noise on torque, sampled uniformly from [-torque_noise_max, torque_noise_max] - "initial_angle_lower": -0.1, # lower bound of initial angle distribution (uniform) - "initial_angle_upper": 0.1, # upper bound of initial angle distribution (uniform) - "initial_velocity_lower": -0.1, # lower bound of initial velocity distribution (uniform) - "initial_velocity_upper": 0.1, # upper bound of initial velocity distribution (uniform) -} - -CONTEXT_BOUNDS = { - "link_length_1": ( - 0.1, - 10, - float, - ), # Links can be shrunken and grown by a factor of 10 - "link_length_2": (0.1, 10, float), - "link_mass_1": ( - 0.1, - 10, - float, - ), # Link mass can be shrunken and grown by a factor of 10 - "link_mass_2": (0.1, 10, float), - "link_com_1": (0, 1, float), # Center of mass can move from one end to the other - "link_com_2": (0, 1, float), - "link_moi": ( - 0.1, - 10, - float, - ), # Moments on inertia can be shrunken and grown by a factor of 10 - "max_velocity_1": ( - 0.4 * np.pi, - 40 * np.pi, - float, - ), # Velocity can vary by a factor of 10 in either direction - "max_velocity_2": (0.9 * np.pi, 90 * np.pi, float), - "torque_noise_max": ( - -1.0, - 1.0, - float, - ), # torque is either {-1., 0., 1}. Applying noise of 1. would be quite extreme - "initial_angle_lower": (-np.inf, np.inf, float), - "initial_angle_upper": (-np.inf, np.inf, float), - "initial_velocity_lower": (-np.inf, np.inf, float), - "initial_velocity_upper": (-np.inf, np.inf, float), -} - - -class CustomAcrobotEnv(AcrobotEnv): - INITIAL_ANGLE_LOWER: float = -0.1 - INITIAL_ANGLE_UPPER: float = 0.1 - INITIAL_VELOCITY_LOWER: float = -0.1 - INITIAL_VELOCITY_UPPER: float = 0.1 - - def reset( - self, - *, - seed: Optional[int] = None, - return_info: bool = False, - options: Optional[dict] = None, - ) -> Union[np.ndarray, tuple[np.ndarray, dict]]: - super().reset(seed=seed) - low = ( - self.INITIAL_ANGLE_LOWER, - self.INITIAL_ANGLE_LOWER, - self.INITIAL_VELOCITY_LOWER, - self.INITIAL_VELOCITY_LOWER, - ) - high = ( - self.INITIAL_ANGLE_UPPER, - self.INITIAL_ANGLE_UPPER, - self.INITIAL_VELOCITY_UPPER, - self.INITIAL_VELOCITY_UPPER, - ) - self.state = self.np_random.uniform(low=low, high=high).astype(np.float32) - if not return_info: - return self._get_ob() - else: - return self._get_ob(), {} - - -class CARLAcrobotEnv(CARLEnv): - def __init__( - self, - env: CustomAcrobotEnv = CustomAcrobotEnv(), - contexts: Contexts = {}, - hide_context: bool = True, - add_gaussian_noise_to_context: bool = False, - gaussian_noise_std_percentage: float = 0.01, - logger: Optional[TrialLogger] = None, - scale_context_features: str = "no", - default_context: Optional[Context] = DEFAULT_CONTEXT, - max_episode_length: int = 500, # from https://github.com/openai/gym/blob/master/gym/envs/__init__.py - state_context_features: Optional[List[str]] = None, - context_mask: Optional[List[str]] = None, - dict_observation_space: bool = False, - context_selector: Optional[ - Union[AbstractSelector, type[AbstractSelector]] - ] = None, - context_selector_kwargs: Optional[Dict] = None, - ): - if not contexts: - contexts = {0: DEFAULT_CONTEXT} - super().__init__( - env=env, - contexts=contexts, - hide_context=hide_context, - add_gaussian_noise_to_context=add_gaussian_noise_to_context, - gaussian_noise_std_percentage=gaussian_noise_std_percentage, - logger=logger, - scale_context_features=scale_context_features, - default_context=default_context, - max_episode_length=max_episode_length, - state_context_features=state_context_features, - dict_observation_space=dict_observation_space, - context_selector=context_selector, - context_selector_kwargs=context_selector_kwargs, - context_mask=context_mask, - ) - self.whitelist_gaussian_noise = list( - DEFAULT_CONTEXT.keys() - ) # allow to augment all values - - def _update_context(self) -> None: - self.env: CustomAcrobotEnv - self.env.LINK_LENGTH_1 = self.context["link_length_1"] - self.env.LINK_LENGTH_2 = self.context["link_length_2"] - self.env.LINK_MASS_1 = self.context["link_mass_1"] - self.env.LINK_MASS_2 = self.context["link_mass_2"] - self.env.LINK_COM_POS_1 = self.context["link_com_1"] - self.env.LINK_COM_POS_2 = self.context["link_com_2"] - self.env.LINK_MOI = self.context["link_moi"] - self.env.MAX_VEL_1 = self.context["max_velocity_1"] - self.env.MAX_VEL_2 = self.context["max_velocity_2"] - self.env.torque_noise_max = self.context["torque_noise_max"] - self.env.INITIAL_ANGLE_LOWER = self.context["initial_angle_lower"] - self.env.INITIAL_ANGLE_UPPER = self.context["initial_angle_upper"] - self.env.INITIAL_VELOCITY_LOWER = self.context["initial_velocity_lower"] - self.env.INITIAL_VELOCITY_UPPER = self.context["initial_velocity_upper"] - - high = np.array( - [1.0, 1.0, 1.0, 1.0, self.env.MAX_VEL_1, self.env.MAX_VEL_2], - dtype=np.float32, - ) - low = -high - self.build_observation_space(low, high, CONTEXT_BOUNDS) diff --git a/carl/envs/classic_control/carl_cartpole.py b/carl/envs/classic_control/carl_cartpole.py deleted file mode 100644 index ba1f1507..00000000 --- a/carl/envs/classic_control/carl_cartpole.py +++ /dev/null @@ -1,125 +0,0 @@ -from typing import Dict, List, Optional, Union - -import numpy as np -from gym.envs.classic_control import CartPoleEnv - -from carl.context.selection import AbstractSelector -from carl.envs.carl_env import CARLEnv -from carl.utils.trial_logger import TrialLogger -from carl.utils.types import Context, Contexts - -DEFAULT_CONTEXT = { - "gravity": 9.8, - "masscart": 1.0, # Should be seen as 100% and scaled accordingly - "masspole": 0.1, # Should be seen as 100% and scaled accordingly - "pole_length": 0.5, # Should be seen as 100% and scaled accordingly - "force_magnifier": 10.0, - "update_interval": 0.02, # Seconds between updates - "initial_state_lower": -0.1, # lower bound of initial state distribution (uniform) (angles and angular velocities) - "initial_state_upper": 0.1, # upper bound of initial state distribution (uniform) (angles and angular velocities) -} - -CONTEXT_BOUNDS = { - "gravity": (0.1, np.inf, float), # Positive gravity - "masscart": (0.1, 10, float), # Cart mass can be varied by a factor of 10 - "masspole": (0.01, 1, float), # Pole mass can be varied by a factor of 10 - "pole_length": (0.05, 5, float), # Pole length can be varied by a factor of 10 - "force_magnifier": (1, 100, int), # Force magnifier can be varied by a factor of 10 - "update_interval": ( - 0.002, - 0.2, - float, - ), # Update interval can be varied by a factor of 10 - "initial_state_lower": (-np.inf, np.inf, float), - "initial_state_upper": (-np.inf, np.inf, float), -} - - -class CustomCartPoleEnv(CartPoleEnv): - def __init__(self) -> None: - super().__init__() - self.initial_state_lower = -0.05 - self.initial_state_upper = 0.05 - - def reset( - self, - *, - seed: Optional[int] = None, - return_info: bool = False, - options: Optional[dict] = None, - ) -> Union[np.ndarray, tuple[np.ndarray, dict]]: - super().reset(seed=seed) - self.state = self.np_random.uniform( - low=self.initial_state_lower, high=self.initial_state_upper, size=(4,) - ) - self.steps_beyond_done = None - if not return_info: - return np.array(self.state, dtype=np.float32) - else: - return np.array(self.state, dtype=np.float32), {} - - -class CARLCartPoleEnv(CARLEnv): - def __init__( - self, - env: CustomCartPoleEnv = CustomCartPoleEnv(), - contexts: Contexts = {}, - hide_context: bool = True, - add_gaussian_noise_to_context: bool = False, - gaussian_noise_std_percentage: float = 0.01, - logger: Optional[TrialLogger] = None, - scale_context_features: str = "no", - default_context: Optional[Context] = DEFAULT_CONTEXT, - max_episode_length: int = 500, # from https://github.com/openai/gym/blob/master/gym/envs/__init__.py - state_context_features: Optional[List[str]] = None, - context_mask: Optional[List[str]] = None, - dict_observation_space: bool = False, - context_selector: Optional[ - Union[AbstractSelector, type[AbstractSelector]] - ] = None, - context_selector_kwargs: Optional[Dict] = None, - ): - if not contexts: - contexts = {0: DEFAULT_CONTEXT} - super().__init__( - env=env, - contexts=contexts, - hide_context=hide_context, - add_gaussian_noise_to_context=add_gaussian_noise_to_context, - gaussian_noise_std_percentage=gaussian_noise_std_percentage, - logger=logger, - scale_context_features=scale_context_features, - default_context=default_context, - max_episode_length=max_episode_length, - state_context_features=state_context_features, - dict_observation_space=dict_observation_space, - context_selector=context_selector, - context_selector_kwargs=context_selector_kwargs, - context_mask=context_mask, - ) - self.whitelist_gaussian_noise = list( - DEFAULT_CONTEXT.keys() - ) # allow to augment all values - - def _update_context(self) -> None: - self.env: CustomCartPoleEnv - self.env.gravity = self.context["gravity"] - self.env.masscart = self.context["masscart"] - self.env.masspole = self.context["masspole"] - self.env.length = self.context["pole_length"] - self.env.force_mag = self.context["force_magnifier"] - self.env.tau = self.context["update_interval"] - self.env.initial_state_lower = self.context["initial_state_lower"] - self.env.initial_state_upper = self.context["initial_state_upper"] - - high = np.array( - [ - self.env.x_threshold * 2, - np.finfo(np.float32).max, - self.env.theta_threshold_radians * 2, - np.finfo(np.float32).max, - ], - dtype=np.float32, - ) - low = -high - self.build_observation_space(low, high, CONTEXT_BOUNDS) diff --git a/carl/envs/classic_control/carl_mountaincar.py b/carl/envs/classic_control/carl_mountaincar.py deleted file mode 100644 index 0407ea67..00000000 --- a/carl/envs/classic_control/carl_mountaincar.py +++ /dev/null @@ -1,158 +0,0 @@ -from typing import Dict, List, Optional, Tuple, Union - -import gym.envs.classic_control as gccenvs -import numpy as np - -from carl.context.selection import AbstractSelector -from carl.envs.carl_env import CARLEnv -from carl.utils.trial_logger import TrialLogger -from carl.utils.types import Context, Contexts - -DEFAULT_CONTEXT = { - "min_position": -1.2, # unit? - "max_position": 0.6, # unit? - "max_speed": 0.07, # unit? - "goal_position": 0.5, # unit? - "goal_velocity": 0, # unit? - "force": 0.001, # unit? - "gravity": 0.0025, # unit? - "min_position_start": -0.6, - "max_position_start": -0.4, - "min_velocity_start": 0.0, - "max_velocity_start": 0.0, -} - -CONTEXT_BOUNDS = { - "min_position": (-np.inf, np.inf, float), - "max_position": (-np.inf, np.inf, float), - "max_speed": (0, np.inf, float), - "goal_position": (-np.inf, np.inf, float), - "goal_velocity": (-np.inf, np.inf, float), - "force": (-np.inf, np.inf, float), - "gravity": (0, np.inf, float), - "min_position_start": (-np.inf, np.inf, float), - "max_position_start": (-np.inf, np.inf, float), - "min_velocity_start": (-np.inf, np.inf, float), - "max_velocity_start": (-np.inf, np.inf, float), -} - - -class CustomMountainCarEnv(gccenvs.mountain_car.MountainCarEnv): - def __init__(self, goal_velocity: float = 0.0): - super(CustomMountainCarEnv, self).__init__(goal_velocity=goal_velocity) - self.min_position_start = -0.6 - self.max_position_start = -0.4 - self.min_velocity_start = 0.0 - self.max_velocity_start = 0.0 - self.state: np.ndarray # type: ignore [assignment] - - def sample_initial_state(self) -> np.ndarray: - return np.array( - [ - self.np_random.uniform( - low=self.min_position_start, high=self.max_position_start - ), - self.np_random.uniform( - low=self.min_velocity_start, high=self.max_velocity_start - ), - ] - ) - - def reset( - self, - *, - seed: Optional[int] = None, - return_info: bool = False, - options: Optional[dict] = None, - ) -> Union[np.ndarray, tuple[np.ndarray, dict]]: - super().reset(seed=seed) - self.state = self.sample_initial_state() - if not return_info: - return np.array(self.state, dtype=np.float32) - else: - return np.array(self.state, dtype=np.float32), {} - - def step(self, action: int) -> Tuple[np.ndarray, float, bool, Dict]: - state, reward, done, info = super().step(action) - return ( - state.squeeze(), - reward, - done, - info, - ) # TODO something weird is happening such that the state gets shape (2,1) instead of (2,) - - -class CARLMountainCarEnv(CARLEnv): - def __init__( - self, - env: CustomMountainCarEnv = CustomMountainCarEnv(), - contexts: Contexts = {}, - hide_context: bool = True, - add_gaussian_noise_to_context: bool = False, - gaussian_noise_std_percentage: float = 0.01, - logger: Optional[TrialLogger] = None, - scale_context_features: str = "no", - default_context: Optional[Context] = DEFAULT_CONTEXT, - max_episode_length: int = 200, # from https://github.com/openai/gym/blob/master/gym/envs/__init__.py - state_context_features: Optional[List[str]] = None, - context_mask: Optional[List[str]] = None, - dict_observation_space: bool = False, - context_selector: Optional[ - Union[AbstractSelector, type[AbstractSelector]] - ] = None, - context_selector_kwargs: Optional[Dict] = None, - ): - """ - - Parameters - ---------- - env: gym.Env, optional - Defaults to classic control environment mountain car from gym (MountainCarEnv). - contexts: List[Dict], optional - Different contexts / different environment parameter settings. - instance_mode: str, optional - """ - if not contexts: - contexts = {0: DEFAULT_CONTEXT} - super().__init__( - env=env, - contexts=contexts, - hide_context=hide_context, - add_gaussian_noise_to_context=add_gaussian_noise_to_context, - gaussian_noise_std_percentage=gaussian_noise_std_percentage, - logger=logger, - scale_context_features=scale_context_features, - default_context=default_context, - max_episode_length=max_episode_length, - state_context_features=state_context_features, - dict_observation_space=dict_observation_space, - context_selector=context_selector, - context_selector_kwargs=context_selector_kwargs, - context_mask=context_mask, - ) - self.whitelist_gaussian_noise = list( - DEFAULT_CONTEXT.keys() - ) # allow to augment all values - - def _update_context(self) -> None: - self.env: CustomMountainCarEnv - self.env.min_position = self.context["min_position"] - self.env.max_position = self.context["max_position"] - self.env.max_speed = self.context["max_speed"] - self.env.goal_position = self.context["goal_position"] - self.env.goal_velocity = self.context["goal_velocity"] - self.env.min_position_start = self.context["min_position_start"] - self.env.max_position_start = self.context["max_position_start"] - self.env.min_velocity_start = self.context["min_velocity_start"] - self.env.max_velocity_start = self.context["max_velocity_start"] - self.env.force = self.context["force"] - self.env.gravity = self.context["gravity"] - - self.low = np.array( - [self.env.min_position, -self.env.max_speed], dtype=np.float32 - ).squeeze() - self.high = np.array( - [self.env.max_position, self.env.max_speed], dtype=np.float32 - ).squeeze() - - self.build_observation_space(self.low, self.high, CONTEXT_BOUNDS) diff --git a/carl/envs/classic_control/carl_mountaincarcontinuous.py b/carl/envs/classic_control/carl_mountaincarcontinuous.py deleted file mode 100644 index 9d833236..00000000 --- a/carl/envs/classic_control/carl_mountaincarcontinuous.py +++ /dev/null @@ -1,139 +0,0 @@ -from typing import Dict, List, Optional, Union - -import gym.envs.classic_control as gccenvs -import numpy as np - -from carl.context.selection import AbstractSelector -from carl.envs.carl_env import CARLEnv -from carl.utils.trial_logger import TrialLogger -from carl.utils.types import Context, Contexts - -DEFAULT_CONTEXT = { - "min_position": -1.2, - "max_position": 0.6, - "max_speed": 0.07, - "goal_position": 0.45, - "goal_velocity": 0.0, - "power": 0.0015, - # "gravity": 0.0025, # currently hardcoded in step - "min_position_start": -0.6, - "max_position_start": -0.4, - "min_velocity_start": 0.0, - "max_velocity_start": 0.0, -} -CONTEXT_BOUNDS = { - "min_position": (-np.inf, np.inf, float), - "max_position": (-np.inf, np.inf, float), - "max_speed": (0, np.inf, float), - "goal_position": (-np.inf, np.inf, float), - "goal_velocity": (-np.inf, np.inf, float), - "power": (-np.inf, np.inf, float), - # "force": (-np.inf, np.inf), - # "gravity": (0, np.inf), - "min_position_start": (-np.inf, np.inf, float), # TODO need to check these - "max_position_start": (-np.inf, np.inf, float), - "min_velocity_start": (-np.inf, np.inf, float), - "max_velocity_start": (-np.inf, np.inf, float), -} - - -class CustomMountainCarContinuousEnv( - gccenvs.continuous_mountain_car.Continuous_MountainCarEnv -): - def __init__(self, goal_velocity: float = 0.0): - super(CustomMountainCarContinuousEnv, self).__init__( - goal_velocity=goal_velocity - ) - self.min_position_start = -0.6 - self.max_position_start = -0.4 - self.min_velocity_start = 0.0 - self.max_velocity_start = 0.0 - - def reset_state(self) -> np.ndarray: - return np.array( - [ - self.np_random.uniform( - low=self.min_position_start, high=self.max_position_start - ), # sample start position - self.np_random.uniform( - low=self.min_velocity_start, high=self.max_velocity_start - ), # sample start velocity - ] - ) - - -class CARLMountainCarContinuousEnv(CARLEnv): - def __init__( - self, - env: CustomMountainCarContinuousEnv = CustomMountainCarContinuousEnv(), - contexts: Contexts = {}, - hide_context: bool = True, - add_gaussian_noise_to_context: bool = True, - gaussian_noise_std_percentage: float = 0.01, - logger: Optional[TrialLogger] = None, - scale_context_features: str = "no", - default_context: Optional[Context] = DEFAULT_CONTEXT, - max_episode_length: int = 999, # from https://github.com/openai/gym/blob/master/gym/envs/__init__.py - state_context_features: Optional[List[str]] = None, - context_mask: Optional[List[str]] = None, - dict_observation_space: bool = False, - context_selector: Optional[ - Union[AbstractSelector, type[AbstractSelector]] - ] = None, - context_selector_kwargs: Optional[Dict] = None, - ): - """ - - Parameters - ---------- - env: gym.Env, optional - Defaults to classic control environment mountain car from gym (MountainCarEnv). - contexts: List[Dict], optional - Different contexts / different environment parameter settings. - instance_mode: str, optional - """ - if not contexts: - contexts = {0: DEFAULT_CONTEXT} - super().__init__( - env=env, - contexts=contexts, - hide_context=hide_context, - add_gaussian_noise_to_context=add_gaussian_noise_to_context, - gaussian_noise_std_percentage=gaussian_noise_std_percentage, - logger=logger, - scale_context_features=scale_context_features, - default_context=default_context, - max_episode_length=max_episode_length, - state_context_features=state_context_features, - dict_observation_space=dict_observation_space, - context_selector=context_selector, - context_selector_kwargs=context_selector_kwargs, - context_mask=context_mask, - ) - self.whitelist_gaussian_noise = list( - DEFAULT_CONTEXT.keys() - ) # allow to augment all values - - def _update_context(self) -> None: - self.env: CustomMountainCarContinuousEnv - self.env.min_position = self.context["min_position"] - self.env.max_position = self.context["max_position"] - self.env.max_speed = self.context["max_speed"] - self.env.goal_position = self.context["goal_position"] - self.env.goal_velocity = self.context["goal_velocity"] - self.env.min_position_start = self.context["min_position_start"] - self.env.max_position_start = self.context["max_position_start"] - self.env.min_velocity_start = self.context["min_velocity_start"] - self.env.max_velocity_start = self.context["max_velocity_start"] - self.env.power = self.context["power"] - # self.env.force = self.context["force"] - # self.env.gravity = self.context["gravity"] - - self.low = np.array( - [self.env.min_position, -self.env.max_speed], dtype=np.float32 - ).squeeze() - self.high = np.array( - [self.env.max_position, self.env.max_speed], dtype=np.float32 - ).squeeze() - - self.build_observation_space(self.low, self.high, CONTEXT_BOUNDS) diff --git a/carl/envs/classic_control/carl_pendulum.py b/carl/envs/classic_control/carl_pendulum.py deleted file mode 100644 index 6a293020..00000000 --- a/carl/envs/classic_control/carl_pendulum.py +++ /dev/null @@ -1,121 +0,0 @@ -from typing import Dict, List, Optional, Union - -import gym.envs.classic_control as gccenvs -import numpy as np - -from carl.context.selection import AbstractSelector -from carl.envs.carl_env import CARLEnv -from carl.utils.trial_logger import TrialLogger -from carl.utils.types import Context, Contexts - -DEFAULT_CONTEXT = { - "max_speed": 8.0, - "dt": 0.05, - "g": 10.0, - "m": 1.0, - "l": 1.0, - "initial_angle_max": np.pi, # Upper bound for uniform distribution to sample from - "initial_velocity_max": 1, # Upper bound for uniform distribution to sample from - # The lower bound will be the negative value. -} - -CONTEXT_BOUNDS = { - "max_speed": (-np.inf, np.inf, float), - "dt": (0, np.inf, float), - "g": (0, np.inf, float), - "m": (1e-6, np.inf, float), - "l": (1e-6, np.inf, float), - "initial_angle_max": (0, np.inf, float), - "initial_velocity_max": (0, np.inf, float), -} - - -class CustomPendulum(gccenvs.pendulum.PendulumEnv): - def __init__(self, g: float = 10.0): - super(CustomPendulum, self).__init__(g=g) - self.initial_angle_max = DEFAULT_CONTEXT["initial_angle_max"] - self.initial_velocity_max = DEFAULT_CONTEXT["initial_velocity_max"] - - def reset( - self, - *, - seed: Optional[int] = None, - return_info: bool = False, - options: Optional[dict] = None, - ) -> Union[np.ndarray, tuple[np.ndarray, dict]]: - super().reset(seed=seed) - high = np.array([self.initial_angle_max, self.initial_velocity_max]) - self.state = self.np_random.uniform(low=-high, high=high) - self.last_u = None - if not return_info: - return self._get_obs() - else: - return self._get_obs(), {} - - -class CARLPendulumEnv(CARLEnv): - def __init__( - self, - env: CustomPendulum = CustomPendulum(), - contexts: Contexts = {}, - hide_context: bool = True, - add_gaussian_noise_to_context: bool = False, - gaussian_noise_std_percentage: float = 0.01, - logger: Optional[TrialLogger] = None, - scale_context_features: str = "no", - default_context: Optional[Context] = DEFAULT_CONTEXT, - max_episode_length: int = 200, # from https://github.com/openai/gym/blob/master/gym/envs/__init__.py - state_context_features: Optional[List[str]] = None, - context_mask: Optional[List[str]] = None, - dict_observation_space: bool = False, - context_selector: Optional[ - Union[AbstractSelector, type[AbstractSelector]] - ] = None, - context_selector_kwargs: Optional[Dict] = None, - ): - """ - Max torque is not a context feature because it changes the action space. - - Parameters - ---------- - env - contexts - instance_mode - hide_context - add_gaussian_noise_to_context - gaussian_noise_std_percentage - """ - if not contexts: - contexts = {0: DEFAULT_CONTEXT} - super().__init__( - env=env, - contexts=contexts, - hide_context=hide_context, - add_gaussian_noise_to_context=add_gaussian_noise_to_context, - gaussian_noise_std_percentage=gaussian_noise_std_percentage, - logger=logger, - scale_context_features=scale_context_features, - default_context=default_context, - max_episode_length=max_episode_length, - state_context_features=state_context_features, - dict_observation_space=dict_observation_space, - context_selector=context_selector, - context_selector_kwargs=context_selector_kwargs, - context_mask=context_mask, - ) - self.whitelist_gaussian_noise = list( - DEFAULT_CONTEXT.keys() - ) # allow to augment all values - - def _update_context(self) -> None: - self.env: CustomPendulum - self.env.max_speed = self.context["max_speed"] - self.env.dt = self.context["dt"] - self.env.l = self.context["l"] # noqa: E741 ambiguous variable name - self.env.m = self.context["m"] - self.env.g = self.context["g"] - self.env.initial_angle_max = self.context["initial_angle_max"] - self.env.initial_velocity_max = self.context["initial_velocity_max"] - - high = np.array([1.0, 1.0, self.max_speed], dtype=np.float32) - self.build_observation_space(-high, high, CONTEXT_BOUNDS) diff --git a/carl/envs/dmc/README.md b/carl/envs/dmc/README.md new file mode 100644 index 00000000..1ab21757 --- /dev/null +++ b/carl/envs/dmc/README.md @@ -0,0 +1,10 @@ +# Headless Rendering +If you have problems with OpenGL, this helped: +Set this in your script +```python +os.environ['DISABLE_MUJOCO_RENDERING'] = '1' +os.environ['MUJOCO_GL'] = 'osmesa' +os.environ['PYOPENGL_PLATFORM'] = 'osmesa' +``` + +And set ErrorChecker to None in `OpenGL/raw/GL/_errors.py`. \ No newline at end of file diff --git a/carl/envs/dmc/__init__.py b/carl/envs/dmc/__init__.py index 03225e66..430665fe 100644 --- a/carl/envs/dmc/__init__.py +++ b/carl/envs/dmc/__init__.py @@ -1,19 +1,13 @@ # flake8: noqa: F401 # Contexts and bounds by name -from carl.envs.dmc.carl_dm_finger import CONTEXT_BOUNDS as CARLDmcFingerEnv_bounds -from carl.envs.dmc.carl_dm_finger import CONTEXT_MASK as CARLDmcFingerEnv_mask -from carl.envs.dmc.carl_dm_finger import DEFAULT_CONTEXT as CARLDmcFingerEnv_defaults from carl.envs.dmc.carl_dm_finger import CARLDmcFingerEnv -from carl.envs.dmc.carl_dm_fish import CONTEXT_BOUNDS as CARLDmcFishEnv_bounds -from carl.envs.dmc.carl_dm_fish import CONTEXT_MASK as CARLDmcFishEnv_mask -from carl.envs.dmc.carl_dm_fish import DEFAULT_CONTEXT as CARLDmcFishEnv_defaults from carl.envs.dmc.carl_dm_fish import CARLDmcFishEnv -from carl.envs.dmc.carl_dm_quadruped import CONTEXT_BOUNDS as CARLDmcQuadrupedEnv_bounds -from carl.envs.dmc.carl_dm_quadruped import CONTEXT_MASK as CARLDmcQuadrupedEnv_mask -from carl.envs.dmc.carl_dm_quadruped import ( - DEFAULT_CONTEXT as CARLDmcQuadrupedEnv_defaults, -) from carl.envs.dmc.carl_dm_quadruped import CARLDmcQuadrupedEnv -from carl.envs.dmc.carl_dm_walker import CONTEXT_MASK as CARLDmcWalkerEnv_mask -from carl.envs.dmc.carl_dm_walker import DEFAULT_CONTEXT as CARLDmcWalkerEnv_defaults from carl.envs.dmc.carl_dm_walker import CARLDmcWalkerEnv + +__all__ = [ + "CARLDmcFingerEnv", + "CARLDmcFishEnv", + "CARLDmcQuadrupedEnv", + "CARLDmcWalkerEnv", +] diff --git a/carl/envs/dmc/carl_dm_finger.py b/carl/envs/dmc/carl_dm_finger.py index ed8469a5..b2604fec 100644 --- a/carl/envs/dmc/carl_dm_finger.py +++ b/carl/envs/dmc/carl_dm_finger.py @@ -1,103 +1,68 @@ -from typing import Dict, List, Optional, Union - import numpy as np -from carl.context.selection import AbstractSelector +from carl.context.context_space import ContextFeature, UniformFloatContextFeature from carl.envs.dmc.carl_dmcontrol import CARLDmcEnv -from carl.envs.dmc.dmc_tasks.fish import STEP_LIMIT # type: ignore -from carl.utils.trial_logger import TrialLogger -from carl.utils.types import Context, Contexts - -DEFAULT_CONTEXT = { - "gravity": -9.81, # Gravity is disabled via flag - "friction_tangential": 1, # Scaling factor for tangential friction of all geoms (objects) - "friction_torsional": 1, # Scaling factor for torsional friction of all geoms (objects) - "friction_rolling": 1, # Scaling factor for rolling friction of all geoms (objects) - "timestep": 0.004, # Seconds between updates - "joint_damping": 1.0, # Scaling factor for all joints - "joint_stiffness": 0.0, - "actuator_strength": 1, # Scaling factor for all actuators in the model - "density": 5000.0, - "viscosity": 0.0, - "geom_density": 1.0, # No effect, because no gravity - "wind_x": 0.0, - "wind_y": 0.0, - "wind_z": 0.0, - "limb_length_0": 0.17, - "limb_length_1": 0.16, - "spinner_radius": 0.04, - "spinner_length": 0.18, -} - -CONTEXT_BOUNDS = { - "gravity": (-np.inf, -0.1, float), - "friction_tangential": (0, np.inf, float), - "friction_torsional": (0, np.inf, float), - "friction_rolling": (0, np.inf, float), - "timestep": ( - 0.001, - 0.1, - float, - ), - "joint_damping": (0, np.inf, float), - "joint_stiffness": (0, np.inf, float), - "actuator_strength": (0, np.inf, float), - "density": (0, np.inf, float), - "viscosity": (0, np.inf, float), - "geom_density": (0, np.inf, float), - "wind_x": (-np.inf, np.inf, float), - "wind_y": (-np.inf, np.inf, float), - "wind_z": (-np.inf, np.inf, float), - "limb_length_0": (0.01, 0.2, float), - "limb_length_1": (0.01, 0.2, float), - "spinner_radius": (0.01, 0.05, float), - "spinner_length": (0.01, 0.4, float), -} - -CONTEXT_MASK = [ - "gravity", - "geom_density", - "wind_x", - "wind_y", - "wind_z", -] class CARLDmcFingerEnv(CARLDmcEnv): - def __init__( - self, - domain: str = "finger", - task: str = "spin_context", - contexts: Contexts = {}, - context_mask: Optional[List[str]] = [], - hide_context: bool = True, - add_gaussian_noise_to_context: bool = False, - gaussian_noise_std_percentage: float = 0.01, - logger: Optional[TrialLogger] = None, - scale_context_features: str = "no", - default_context: Optional[Context] = DEFAULT_CONTEXT, - max_episode_length: int = STEP_LIMIT, - state_context_features: Optional[List[str]] = None, - dict_observation_space: bool = False, - context_selector: Optional[ - Union[AbstractSelector, type[AbstractSelector]] - ] = None, - context_selector_kwargs: Optional[Dict] = None, - ): - super().__init__( - domain=domain, - task=task, - contexts=contexts, - context_mask=context_mask, - hide_context=hide_context, - add_gaussian_noise_to_context=add_gaussian_noise_to_context, - gaussian_noise_std_percentage=gaussian_noise_std_percentage, - logger=logger, - scale_context_features=scale_context_features, - default_context=default_context, - max_episode_length=max_episode_length, - state_context_features=state_context_features, - dict_observation_space=dict_observation_space, - context_selector=context_selector, - context_selector_kwargs=context_selector_kwargs, - ) + domain = "finger" + task = "spin_context" + + @staticmethod + def get_context_features() -> dict[str, ContextFeature]: + return { + "gravity": UniformFloatContextFeature( + "gravity", lower=-np.inf, upper=-0.1, default_value=-9.81 + ), + "friction_torsional": UniformFloatContextFeature( + "friction_torsional", lower=0, upper=np.inf, default_value=1.0 + ), + "friction_rolling": UniformFloatContextFeature( + "friction_rolling", lower=0, upper=np.inf, default_value=1.0 + ), + "friction_tangential": UniformFloatContextFeature( + "friction_tangential", lower=0, upper=np.inf, default_value=1.0 + ), + "timestep": UniformFloatContextFeature( + "timestep", lower=0.001, upper=0.1, default_value=0.0025 + ), + "joint_damping": UniformFloatContextFeature( + "joint_damping", lower=0.0, upper=np.inf, default_value=1.0 + ), + "joint_stiffness": UniformFloatContextFeature( + "joint_stiffness", lower=0.0, upper=np.inf, default_value=0.0 + ), + "actuator_strength": UniformFloatContextFeature( + "actuator_strength", lower=0.0, upper=np.inf, default_value=1.0 + ), + "density": UniformFloatContextFeature( + "density", lower=0.0, upper=np.inf, default_value=0.0 + ), + "viscosity": UniformFloatContextFeature( + "viscosity", lower=0.0, upper=np.inf, default_value=0.0 + ), + "geom_density": UniformFloatContextFeature( + "geom_density", lower=0.0, upper=np.inf, default_value=1.0 + ), + "wind_x": UniformFloatContextFeature( + "wind_x", lower=-np.inf, upper=np.inf, default_value=0.0 + ), + "wind_y": UniformFloatContextFeature( + "wind_y", lower=-np.inf, upper=np.inf, default_value=0.0 + ), + "wind_z": UniformFloatContextFeature( + "wind_z", lower=-np.inf, upper=np.inf, default_value=0.0 + ), + "limb_length_0": UniformFloatContextFeature( + "limb_length_0", lower=0.01, upper=0.2, default_value=0.17 + ), + "limb_length_1": UniformFloatContextFeature( + "limb_length_1", lower=0.01, upper=0.2, default_value=0.16 + ), + "spinner_radius": UniformFloatContextFeature( + "spinner_radius", lower=0.01, upper=0.05, default_value=0.04 + ), + "spinner_length": UniformFloatContextFeature( + "spinner_length", lower=0.01, upper=0.4, default_value=0.18 + ), + } diff --git a/carl/envs/dmc/carl_dm_fish.py b/carl/envs/dmc/carl_dm_fish.py index b7886baa..619364a8 100644 --- a/carl/envs/dmc/carl_dm_fish.py +++ b/carl/envs/dmc/carl_dm_fish.py @@ -1,95 +1,56 @@ -from typing import Dict, List, Optional, Union - import numpy as np -from carl.context.selection import AbstractSelector +from carl.context.context_space import ContextFeature, UniformFloatContextFeature from carl.envs.dmc.carl_dmcontrol import CARLDmcEnv -from carl.envs.dmc.dmc_tasks.fish import STEP_LIMIT # type: ignore -from carl.utils.trial_logger import TrialLogger -from carl.utils.types import Context, Contexts - -DEFAULT_CONTEXT = { - "gravity": -9.81, # Gravity is disabled via flag - "friction_tangential": 1, # Scaling factor for tangential friction of all geoms (objects) - "friction_torsional": 1, # Scaling factor for torsional friction of all geoms (objects) - "friction_rolling": 1, # Scaling factor for rolling friction of all geoms (objects) - "timestep": 0.004, # Seconds between updates - "joint_damping": 1.0, # Scaling factor for all joints - "joint_stiffness": 0.0, - "actuator_strength": 1, # Scaling factor for all actuators in the model - "density": 5000.0, - "viscosity": 0.0, - "geom_density": 1.0, # No effect, because no gravity - "wind_x": 0.0, - "wind_y": 0.0, - "wind_z": 0.0, -} - -CONTEXT_BOUNDS = { - "gravity": (-np.inf, -0.1, float), - "friction_tangential": (0, np.inf, float), - "friction_torsional": (0, np.inf, float), - "friction_rolling": (0, np.inf, float), - "timestep": ( - 0.001, - 0.1, - float, - ), - "joint_damping": (0, np.inf, float), - "joint_stiffness": (0, np.inf, float), - "actuator_strength": (0, np.inf, float), - "density": (0, np.inf, float), - "viscosity": (0, np.inf, float), - "geom_density": (0, np.inf, float), - "wind_x": (-np.inf, np.inf, float), - "wind_y": (-np.inf, np.inf, float), - "wind_z": (-np.inf, np.inf, float), -} - -CONTEXT_MASK = [ - "gravity", - "geom_density", - "wind_x", - "wind_y", - "wind_z", -] class CARLDmcFishEnv(CARLDmcEnv): - def __init__( - self, - domain: str = "fish", - task: str = "swim_context", - contexts: Contexts = {}, - context_mask: Optional[List[str]] = [], - hide_context: bool = True, - add_gaussian_noise_to_context: bool = False, - gaussian_noise_std_percentage: float = 0.01, - logger: Optional[TrialLogger] = None, - scale_context_features: str = "no", - default_context: Optional[Context] = DEFAULT_CONTEXT, - max_episode_length: int = STEP_LIMIT, - state_context_features: Optional[List[str]] = None, - dict_observation_space: bool = False, - context_selector: Optional[ - Union[AbstractSelector, type[AbstractSelector]] - ] = None, - context_selector_kwargs: Optional[Dict] = None, - ): - super().__init__( - domain=domain, - task=task, - contexts=contexts, - context_mask=context_mask, - hide_context=hide_context, - add_gaussian_noise_to_context=add_gaussian_noise_to_context, - gaussian_noise_std_percentage=gaussian_noise_std_percentage, - logger=logger, - scale_context_features=scale_context_features, - default_context=default_context, - max_episode_length=max_episode_length, - state_context_features=state_context_features, - dict_observation_space=dict_observation_space, - context_selector=context_selector, - context_selector_kwargs=context_selector_kwargs, - ) + domain = "fish" + task = "swim_context" + + @staticmethod + def get_context_features() -> dict[str, ContextFeature]: + return { + "gravity": UniformFloatContextFeature( + "gravity", lower=-np.inf, upper=-0.1, default_value=-9.81 + ), + "friction_torsional": UniformFloatContextFeature( + "friction_torsional", lower=0, upper=np.inf, default_value=1.0 + ), + "friction_rolling": UniformFloatContextFeature( + "friction_rolling", lower=0, upper=np.inf, default_value=1.0 + ), + "friction_tangential": UniformFloatContextFeature( + "friction_tangential", lower=0, upper=np.inf, default_value=1.0 + ), + "timestep": UniformFloatContextFeature( + "timestep", lower=0.001, upper=0.1, default_value=0.004 + ), + "joint_damping": UniformFloatContextFeature( + "joint_damping", lower=0.0, upper=np.inf, default_value=1.0 + ), + "joint_stiffness": UniformFloatContextFeature( + "joint_stiffness", lower=0.0, upper=np.inf, default_value=0.0 + ), + "actuator_strength": UniformFloatContextFeature( + "actuator_strength", lower=0.0, upper=np.inf, default_value=1.0 + ), + "density": UniformFloatContextFeature( + "density", lower=0.0, upper=np.inf, default_value=5000.0 + ), + "viscosity": UniformFloatContextFeature( + "viscosity", lower=0.0, upper=np.inf, default_value=0.0 + ), + "geom_density": UniformFloatContextFeature( + "geom_density", lower=0.0, upper=np.inf, default_value=1.0 + ), + "wind_x": UniformFloatContextFeature( + "wind_x", lower=-np.inf, upper=np.inf, default_value=0.0 + ), + "wind_y": UniformFloatContextFeature( + "wind_y", lower=-np.inf, upper=np.inf, default_value=0.0 + ), + "wind_z": UniformFloatContextFeature( + "wind_z", lower=-np.inf, upper=np.inf, default_value=0.0 + ), + } diff --git a/carl/envs/dmc/carl_dm_quadruped.py b/carl/envs/dmc/carl_dm_quadruped.py index 29554d98..8d1fbdd2 100644 --- a/carl/envs/dmc/carl_dm_quadruped.py +++ b/carl/envs/dmc/carl_dm_quadruped.py @@ -1,93 +1,56 @@ -from typing import Dict, List, Optional, Union - import numpy as np -from carl.context.selection import AbstractSelector +from carl.context.context_space import ContextFeature, UniformFloatContextFeature from carl.envs.dmc.carl_dmcontrol import CARLDmcEnv -from carl.envs.dmc.dmc_tasks.quadruped import STEP_LIMIT # type: ignore -from carl.utils.trial_logger import TrialLogger -from carl.utils.types import Context, Contexts - -DEFAULT_CONTEXT = { - "gravity": -9.81, - "friction_tangential": 1.0, # Scaling factor for tangential friction of all geoms (objects) - "friction_torsional": 1.0, # Scaling factor for torsional friction of all geoms (objects) - "friction_rolling": 1.0, # Scaling factor for rolling friction of all geoms (objects) - "timestep": 0.005, # Seconds between updates - "joint_damping": 1.0, # Scaling factor for all joints - "joint_stiffness": 0.0, - "actuator_strength": 1, # Scaling factor for all actuators in the model - "density": 0.0, - "viscosity": 0.0, - "geom_density": 1.0, # Scaling factor for all geom (objects) densities - "wind_x": 0.0, - "wind_y": 0.0, - "wind_z": 0.0, -} - -CONTEXT_BOUNDS = { - "gravity": (-np.inf, -0.1, float), - "friction_tangential": (0, np.inf, float), - "friction_torsional": (0, np.inf, float), - "friction_rolling": (0, np.inf, float), - "timestep": ( - 0.001, - 0.1, - float, - ), - "joint_damping": (0, np.inf, float), - "joint_stiffness": (0, np.inf, float), - "actuator_strength": (0, np.inf, float), - "density": (0, np.inf, float), - "viscosity": (0, np.inf, float), - "geom_density": (0, np.inf, float), - "wind_x": (-np.inf, np.inf, float), - "wind_y": (-np.inf, np.inf, float), - "wind_z": (-np.inf, np.inf, float), -} - -CONTEXT_MASK = [ - "wind_x", - "wind_y", - "wind_z", -] class CARLDmcQuadrupedEnv(CARLDmcEnv): - def __init__( - self, - domain: str = "quadruped", - task: str = "walk_context", - contexts: Contexts = {}, - context_mask: Optional[List[str]] = [], - hide_context: bool = True, - add_gaussian_noise_to_context: bool = False, - gaussian_noise_std_percentage: float = 0.01, - logger: Optional[TrialLogger] = None, - scale_context_features: str = "no", - default_context: Optional[Context] = DEFAULT_CONTEXT, - max_episode_length: int = STEP_LIMIT, - state_context_features: Optional[List[str]] = None, - dict_observation_space: bool = False, - context_selector: Optional[ - Union[AbstractSelector, type[AbstractSelector]] - ] = None, - context_selector_kwargs: Optional[Dict] = None, - ): - super().__init__( - domain=domain, - task=task, - contexts=contexts, - context_mask=context_mask, - hide_context=hide_context, - add_gaussian_noise_to_context=add_gaussian_noise_to_context, - gaussian_noise_std_percentage=gaussian_noise_std_percentage, - logger=logger, - scale_context_features=scale_context_features, - default_context=default_context, - max_episode_length=max_episode_length, - state_context_features=state_context_features, - dict_observation_space=dict_observation_space, - context_selector=context_selector, - context_selector_kwargs=context_selector_kwargs, - ) + domain = "quadruped" + task = "walk_context" + + @staticmethod + def get_context_features() -> dict[str, ContextFeature]: + return { + "gravity": UniformFloatContextFeature( + "gravity", lower=-np.inf, upper=-0.1, default_value=-9.81 + ), + "friction_torsional": UniformFloatContextFeature( + "friction_torsional", lower=0, upper=np.inf, default_value=1.0 + ), + "friction_rolling": UniformFloatContextFeature( + "friction_rolling", lower=0, upper=np.inf, default_value=1.0 + ), + "friction_tangential": UniformFloatContextFeature( + "friction_tangential", lower=0, upper=np.inf, default_value=1.0 + ), + "timestep": UniformFloatContextFeature( + "timestep", lower=0.001, upper=0.1, default_value=0.005 + ), + "joint_damping": UniformFloatContextFeature( + "joint_damping", lower=0.0, upper=np.inf, default_value=1.0 + ), + "joint_stiffness": UniformFloatContextFeature( + "joint_stiffness", lower=0.0, upper=np.inf, default_value=0.0 + ), + "actuator_strength": UniformFloatContextFeature( + "actuator_strength", lower=0.0, upper=np.inf, default_value=1.0 + ), + "density": UniformFloatContextFeature( + "density", lower=0.0, upper=np.inf, default_value=0.0 + ), + "viscosity": UniformFloatContextFeature( + "viscosity", lower=0.0, upper=np.inf, default_value=0.0 + ), + "geom_density": UniformFloatContextFeature( + "geom_density", lower=0.0, upper=np.inf, default_value=1.0 + ), + "wind_x": UniformFloatContextFeature( + "wind_x", lower=-np.inf, upper=np.inf, default_value=0.0 + ), + "wind_y": UniformFloatContextFeature( + "wind_y", lower=-np.inf, upper=np.inf, default_value=0.0 + ), + "wind_z": UniformFloatContextFeature( + "wind_z", lower=-np.inf, upper=np.inf, default_value=0.0 + ), + } diff --git a/carl/envs/dmc/carl_dm_walker.py b/carl/envs/dmc/carl_dm_walker.py index 524aa2a3..97b6d9b1 100644 --- a/carl/envs/dmc/carl_dm_walker.py +++ b/carl/envs/dmc/carl_dm_walker.py @@ -1,93 +1,56 @@ -from typing import Dict, List, Optional, Union - import numpy as np -from carl.context.selection import AbstractSelector +from carl.context.context_space import ContextFeature, UniformFloatContextFeature from carl.envs.dmc.carl_dmcontrol import CARLDmcEnv -from carl.envs.dmc.dmc_tasks.walker import STEP_LIMIT # type: ignore -from carl.utils.trial_logger import TrialLogger -from carl.utils.types import Context, Contexts - -DEFAULT_CONTEXT = { - "gravity": -9.81, - "friction_tangential": 1.0, # Scaling factor for tangential friction of all geoms (objects) - "friction_torsional": 1.0, # Scaling factor for torsional friction of all geoms (objects) - "friction_rolling": 1.0, # Scaling factor for rolling friction of all geoms (objects) - "timestep": 0.0025, # Seconds between updates - "joint_damping": 1.0, # Scaling factor for all joints - "joint_stiffness": 0.0, - "actuator_strength": 1.0, # Scaling factor for all actuators in the model - "density": 0.0, - "viscosity": 0.0, - "geom_density": 1.0, # Scaling factor for all geom (objects) densities - "wind_x": 0.0, - "wind_y": 0.0, - "wind_z": 0.0, -} - -CONTEXT_BOUNDS = { - "gravity": (-np.inf, -0.1, float), - "friction_tangential": (0, np.inf, float), - "friction_torsional": (0, np.inf, float), - "friction_rolling": (0, np.inf, float), - "timestep": ( - 0.001, - 0.1, - float, - ), - "joint_damping": (0, np.inf, float), - "joint_stiffness": (0, np.inf, float), - "actuator_strength": (0, np.inf, float), - "density": (0, np.inf, float), - "viscosity": (0, np.inf, float), - "geom_density": (0, np.inf, float), - "wind_x": (-np.inf, np.inf, float), - "wind_y": (-np.inf, np.inf, float), - "wind_z": (-np.inf, np.inf, float), -} - -CONTEXT_MASK = [ - "wind_x", - "wind_y", - "wind_z", -] class CARLDmcWalkerEnv(CARLDmcEnv): - def __init__( - self, - domain: str = "walker", - task: str = "walk_context", - contexts: Contexts = {}, - context_mask: Optional[List[str]] = [], - hide_context: bool = True, - add_gaussian_noise_to_context: bool = False, - gaussian_noise_std_percentage: float = 0.01, - logger: Optional[TrialLogger] = None, - scale_context_features: str = "no", - default_context: Optional[Context] = DEFAULT_CONTEXT, - max_episode_length: int = STEP_LIMIT, - state_context_features: Optional[List[str]] = None, - dict_observation_space: bool = False, - context_selector: Optional[ - Union[AbstractSelector, type[AbstractSelector]] - ] = None, - context_selector_kwargs: Optional[Dict] = None, - ): - super().__init__( - domain=domain, - task=task, - contexts=contexts, - context_mask=context_mask, - hide_context=hide_context, - add_gaussian_noise_to_context=add_gaussian_noise_to_context, - gaussian_noise_std_percentage=gaussian_noise_std_percentage, - logger=logger, - scale_context_features=scale_context_features, - default_context=default_context, - max_episode_length=max_episode_length, - state_context_features=state_context_features, - dict_observation_space=dict_observation_space, - context_selector=context_selector, - context_selector_kwargs=context_selector_kwargs, - ) + domain = "walker" + task = "walk_context" + + @staticmethod + def get_context_features() -> dict[str, ContextFeature]: + return { + "gravity": UniformFloatContextFeature( + "gravity", lower=-np.inf, upper=-0.1, default_value=-9.81 + ), + "friction_torsional": UniformFloatContextFeature( + "friction_torsional", lower=0, upper=np.inf, default_value=1.0 + ), + "friction_rolling": UniformFloatContextFeature( + "friction_rolling", lower=0, upper=np.inf, default_value=1.0 + ), + "friction_tangential": UniformFloatContextFeature( + "friction_tangential", lower=0, upper=np.inf, default_value=1.0 + ), + "timestep": UniformFloatContextFeature( + "timestep", lower=0.001, upper=0.1, default_value=0.0025 + ), + "joint_damping": UniformFloatContextFeature( + "joint_damping", lower=0.0, upper=np.inf, default_value=1.0 + ), + "joint_stiffness": UniformFloatContextFeature( + "joint_stiffness", lower=0.0, upper=np.inf, default_value=0.0 + ), + "actuator_strength": UniformFloatContextFeature( + "actuator_strength", lower=0.0, upper=np.inf, default_value=1.0 + ), + "density": UniformFloatContextFeature( + "density", lower=0.0, upper=np.inf, default_value=0.0 + ), + "viscosity": UniformFloatContextFeature( + "viscosity", lower=0.0, upper=np.inf, default_value=0.0 + ), + "geom_density": UniformFloatContextFeature( + "geom_density", lower=0.0, upper=np.inf, default_value=1.0 + ), + "wind_x": UniformFloatContextFeature( + "wind_x", lower=-np.inf, upper=np.inf, default_value=0.0 + ), + "wind_y": UniformFloatContextFeature( + "wind_y", lower=-np.inf, upper=np.inf, default_value=0.0 + ), + "wind_z": UniformFloatContextFeature( + "wind_z", lower=-np.inf, upper=np.inf, default_value=0.0 + ), + } diff --git a/carl/envs/dmc/carl_dmcontrol.py b/carl/envs/dmc/carl_dmcontrol.py index 313868fe..a7c52939 100644 --- a/carl/envs/dmc/carl_dmcontrol.py +++ b/carl/envs/dmc/carl_dmcontrol.py @@ -1,11 +1,12 @@ -from typing import Dict, List, Optional, Union +from __future__ import annotations + +from gymnasium import spaces from carl.context.selection import AbstractSelector from carl.envs.carl_env import CARLEnv from carl.envs.dmc.loader import load_dmc_env from carl.envs.dmc.wrappers import MujocoToGymWrapper -from carl.utils.trial_logger import TrialLogger -from carl.utils.types import Context, Contexts +from carl.utils.types import Contexts class CARLDmcEnv(CARLEnv): @@ -31,55 +32,39 @@ class CARLDmcEnv(CARLEnv): def __init__( self, - domain: str, - task: str, - contexts: Contexts, - context_mask: Optional[List[str]], - hide_context: bool, - add_gaussian_noise_to_context: bool, - gaussian_noise_std_percentage: float, - logger: Optional[TrialLogger], - scale_context_features: str, - default_context: Optional[Context], - max_episode_length: int, - state_context_features: Optional[List[str]], - dict_observation_space: bool, - context_selector: Optional[Union[AbstractSelector, type[AbstractSelector]]], - context_selector_kwargs: Optional[Dict], + contexts: Contexts | None = None, + obs_context_features: list[str] | None = None, + obs_context_as_dict: bool = True, + context_selector: AbstractSelector | type[AbstractSelector] | None = None, + context_selector_kwargs: dict = None, + **kwargs, ): # TODO can we have more than 1 env? - if not contexts: - contexts = {0: default_context} # type: ignore - self.domain = domain - self.task = task env = load_dmc_env( domain_name=self.domain, task_name=self.task, context={}, - context_mask=[], environment_kwargs={"flat_observation": True}, ) env = MujocoToGymWrapper(env) + env.observation_space = spaces.Box( + low=env.observation_space.low, + high=env.observation_space.high, + dtype=env.observation_space.dtype, + ) super().__init__( env=env, contexts=contexts, - hide_context=hide_context, - add_gaussian_noise_to_context=add_gaussian_noise_to_context, - gaussian_noise_std_percentage=gaussian_noise_std_percentage, - logger=logger, - scale_context_features=scale_context_features, - default_context=default_context, - max_episode_length=max_episode_length, - state_context_features=state_context_features, - dict_observation_space=dict_observation_space, + obs_context_features=obs_context_features, + obs_context_as_dict=obs_context_as_dict, context_selector=context_selector, context_selector_kwargs=context_selector_kwargs, - context_mask=context_mask, + **kwargs, ) # TODO check gaussian noise on context features self.whitelist_gaussian_noise = list( - default_context.keys() # type: ignore + self.get_context_features().keys() # type: ignore ) # allow to augment all values def _update_context(self) -> None: @@ -87,7 +72,9 @@ def _update_context(self) -> None: domain_name=self.domain, task_name=self.task, context=self.context, - context_mask=self.context_mask, environment_kwargs={"flat_observation": True}, ) self.env = MujocoToGymWrapper(env) + + def render(self): + return self.env.render(mode="rgb_array") diff --git a/carl/envs/dmc/dmc_tasks/__init__.py b/carl/envs/dmc/dmc_tasks/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/carl/envs/dmc/dmc_tasks/finger.py b/carl/envs/dmc/dmc_tasks/finger.py index 9bad7621..e366f1c4 100644 --- a/carl/envs/dmc/dmc_tasks/finger.py +++ b/carl/envs/dmc/dmc_tasks/finger.py @@ -19,8 +19,6 @@ from typing import Any -from multiprocessing.sharedctypes import Value - import numpy as np from dm_control.rl import control # type: ignore from dm_control.suite.finger import ( # type: ignore @@ -45,25 +43,48 @@ def check_constraints( limb_length_1: float, x_spinner: float = 0.2, x_finger: float = -0.2, -) -> None: + raise_error: bool = False, + **kwargs: Any, +) -> bool: + is_okay = True spinner_half_length = spinner_length / 2 # Check if spinner collides with finger hinge distance_spinner_to_fingerhinge = (x_spinner - x_finger) - spinner_half_length if distance_spinner_to_fingerhinge < 0: - raise ValueError( - f"Distance finger to spinner ({distance_spinner_to_fingerhinge}) not big enough, " - f"spinner can't spin. Decrease spinner_length ({spinner_length})." - ) + is_okay = False + if raise_error: + raise ValueError( + f"Distance finger to spinner ({distance_spinner_to_fingerhinge}) not big enough, " + f"spinner can't spin. Decrease spinner_length ({spinner_length})." + ) + is_okay = False + if raise_error: + raise ValueError( + f"Distance finger to spinner ({distance_spinner_to_fingerhinge}) not big enough, " + f"spinner can't spin. Decrease spinner_length ({spinner_length})." + ) # Check if finger can reach spinner (distance should be negative) distance_fingertip_to_spinner = (x_spinner - spinner_half_length) - ( x_finger + limb_length_0 + limb_length_1 ) if distance_fingertip_to_spinner > 0: - raise ValueError( - f"Finger cannot reach spinner ({distance_fingertip_to_spinner}). Increase either " - f"limb_length_0, limb_length_1 or spinner_length." - ) + is_okay = False + if raise_error: + raise ValueError( + f"Finger cannot reach spinner ({distance_fingertip_to_spinner}). Increase either " + f"limb_length_0, limb_length_1 or spinner_length." + ) + is_okay = False + if raise_error: + raise ValueError( + f"Finger cannot reach spinner ({distance_fingertip_to_spinner}). Increase either " + f"limb_length_0, limb_length_1 or spinner_length." + ) + + return is_okay + + return is_okay def get_finger_xml_string( @@ -98,6 +119,7 @@ def get_finger_xml_string( x_spinner=x_spinner, x_finger=x_finger, spinner_length=spinner_length, + raise_error=True, ) proximal_to = -limb_length_0 @@ -181,7 +203,6 @@ def get_finger_xml_string( @SUITE.add("benchmarking") # type: ignore[misc] def spin_context( context: Context = {}, - context_mask: list = [], time_limit: float = _DEFAULT_TIME_LIMIT, random: np.random.RandomState | int | None = None, environment_kwargs: dict | None = None, @@ -190,9 +211,7 @@ def spin_context( xml_string, assets = get_model_and_assets() xml_string = get_finger_xml_string(**context) if context != {}: - xml_string = adapt_context( - xml_string=xml_string, context=context, context_mask=context_mask - ) + xml_string = adapt_context(xml_string=xml_string, context=context) physics = Physics.from_xml_string(xml_string, assets) task = Spin(random=random) environment_kwargs = environment_kwargs or {} @@ -208,7 +227,6 @@ def spin_context( @SUITE.add("benchmarking") # type: ignore[misc] def turn_easy_context( context: Context = {}, - context_mask: list = [], time_limit: float = _DEFAULT_TIME_LIMIT, random: np.random.RandomState | int | None = None, environment_kwargs: dict | None = None, @@ -217,9 +235,7 @@ def turn_easy_context( xml_string, assets = get_model_and_assets() xml_string = get_finger_xml_string(**context) if context != {}: - xml_string = adapt_context( - xml_string=xml_string, context=context, context_mask=context_mask - ) + xml_string = adapt_context(xml_string=xml_string, context=context) physics = Physics.from_xml_string(xml_string, assets) task = Turn(target_radius=_EASY_TARGET_SIZE, random=random) environment_kwargs = environment_kwargs or {} @@ -235,7 +251,6 @@ def turn_easy_context( @SUITE.add("benchmarking") # type: ignore[misc] def turn_hard_context( context: Context = {}, - context_mask: list = [], time_limit: float = _DEFAULT_TIME_LIMIT, random: np.random.RandomState | int | None = None, environment_kwargs: dict | None = None, @@ -244,9 +259,7 @@ def turn_hard_context( xml_string, assets = get_model_and_assets() xml_string = get_finger_xml_string(**context) if context != {}: - xml_string = adapt_context( - xml_string=xml_string, context=context, context_mask=context_mask - ) + xml_string = adapt_context(xml_string=xml_string, context=context) physics = Physics.from_xml_string(xml_string, assets) task = Turn(target_radius=_HARD_TARGET_SIZE, random=random) environment_kwargs = environment_kwargs or {} diff --git a/carl/envs/dmc/dmc_tasks/fish.py b/carl/envs/dmc/dmc_tasks/fish.py index 0442c57c..9d83015d 100644 --- a/carl/envs/dmc/dmc_tasks/fish.py +++ b/carl/envs/dmc/dmc_tasks/fish.py @@ -15,7 +15,7 @@ """Fish Domain.""" -from typing import Dict, List, Optional, Tuple, Union +from typing import Dict, Optional, Tuple, Union import collections @@ -52,7 +52,6 @@ def get_model_and_assets() -> Tuple[bytes, Dict]: @SUITE.add("benchmarking") # type: ignore def upright_context( context: Context = {}, - context_mask: List = [], time_limit: int = _DEFAULT_TIME_LIMIT, random: Union[np.random.RandomState, int, None] = None, environment_kwargs: Optional[Dict] = None, @@ -60,9 +59,7 @@ def upright_context( """Returns the Fish Upright task.""" xml_string, assets = get_model_and_assets() if context != {}: - xml_string = adapt_context( - xml_string=xml_string, context=context, context_mask=context_mask - ) + xml_string = adapt_context(xml_string=xml_string, context=context) physics = Physics.from_xml_string(xml_string, assets) task = Upright(random=random) environment_kwargs = environment_kwargs or {} @@ -78,7 +75,6 @@ def upright_context( @SUITE.add("benchmarking") # type: ignore def swim_context( context: Context = {}, - context_mask: List = [], time_limit: int = _DEFAULT_TIME_LIMIT, random: Union[np.random.RandomState, int, None] = None, environment_kwargs: Optional[Dict] = None, @@ -86,9 +82,7 @@ def swim_context( """Returns the Fish Swim task.""" xml_string, assets = get_model_and_assets() if context != {}: - xml_string = adapt_context( - xml_string=xml_string, context=context, context_mask=context_mask - ) + xml_string = adapt_context(xml_string=xml_string, context=context) physics = Physics.from_xml_string(xml_string, assets) task = Swim(random=random) environment_kwargs = environment_kwargs or {} diff --git a/carl/envs/dmc/dmc_tasks/quadruped.py b/carl/envs/dmc/dmc_tasks/quadruped.py index e67029ec..ffdc07c6 100644 --- a/carl/envs/dmc/dmc_tasks/quadruped.py +++ b/carl/envs/dmc/dmc_tasks/quadruped.py @@ -106,7 +106,6 @@ def make_model( @SUITE.add() # type: ignore def walk_context( context: Context = {}, - context_mask: List = [], time_limit: int = _DEFAULT_TIME_LIMIT, random: Union[np.random.RandomState, int, None] = None, environment_kwargs: Optional[Dict] = None, @@ -114,9 +113,7 @@ def walk_context( """Returns the Walk task with the adapted context.""" xml_string = make_model(floor_size=_DEFAULT_TIME_LIMIT * _WALK_SPEED) if context != {}: - xml_string = adapt_context( - xml_string=xml_string, context=context, context_mask=context_mask - ) + xml_string = adapt_context(xml_string=xml_string, context=context) physics = Physics.from_xml_string(xml_string, common.ASSETS) task = Move(desired_speed=_WALK_SPEED, random=random) environment_kwargs = environment_kwargs or {} @@ -132,7 +129,6 @@ def walk_context( @SUITE.add() # type: ignore def run_context( context: Context = {}, - context_mask: List = [], time_limit: int = _DEFAULT_TIME_LIMIT, random: Union[np.random.RandomState, int, None] = None, environment_kwargs: Optional[Dict] = None, @@ -140,9 +136,7 @@ def run_context( """Returns the Run task with the adapted context.""" xml_string = make_model(floor_size=_DEFAULT_TIME_LIMIT * _RUN_SPEED) if context != {}: - xml_string = adapt_context( - xml_string=xml_string, context=context, context_mask=context_mask - ) + xml_string = adapt_context(xml_string=xml_string, context=context) physics = Physics.from_xml_string(xml_string, common.ASSETS) task = Move(desired_speed=_RUN_SPEED, random=random) environment_kwargs = environment_kwargs or {} @@ -158,7 +152,6 @@ def run_context( @SUITE.add() # type: ignore def escape_context( context: Context = {}, - context_mask: List = [], time_limit: int = _DEFAULT_TIME_LIMIT, random: Union[np.random.RandomState, int, None] = None, environment_kwargs: Optional[Dict] = None, @@ -166,9 +159,7 @@ def escape_context( """Returns the Escape task with the adapted context.""" xml_string = make_model(floor_size=40, terrain=True, rangefinders=True) if context != {}: - xml_string = adapt_context( - xml_string=xml_string, context=context, context_mask=context_mask - ) + xml_string = adapt_context(xml_string=xml_string, context=context) physics = Physics.from_xml_string(xml_string, common.ASSETS) task = Escape(random=random) environment_kwargs = environment_kwargs or {} @@ -184,7 +175,6 @@ def escape_context( @SUITE.add() # type: ignore def fetch_context( context: Context = {}, - context_mask: List = [], time_limit: int = _DEFAULT_TIME_LIMIT, random: Union[np.random.RandomState, int, None] = None, environment_kwargs: Optional[Dict] = None, @@ -192,9 +182,7 @@ def fetch_context( """Returns the Fetch task with the adapted context.""" xml_string = make_model(walls_and_ball=True) if context != {}: - xml_string = adapt_context( - xml_string=xml_string, context=context, context_mask=context_mask - ) + xml_string = adapt_context(xml_string=xml_string, context=context) physics = Physics.from_xml_string(xml_string, common.ASSETS) task = Fetch(random=random) environment_kwargs = environment_kwargs or {} diff --git a/carl/envs/dmc/dmc_tasks/utils.py b/carl/envs/dmc/dmc_tasks/utils.py index ab449618..7482dfbe 100644 --- a/carl/envs/dmc/dmc_tasks/utils.py +++ b/carl/envs/dmc/dmc_tasks/utils.py @@ -1,47 +1,29 @@ from __future__ import annotations -from typing import List - from lxml import etree # type: ignore from carl.utils.types import Context -def adapt_context( - xml_string: bytes, context: Context, context_mask: List = [] -) -> bytes: +def adapt_context(xml_string: bytes, context: Context) -> bytes: """Adapts and returns the xml_string of the model with the given context.""" - def check_okay_to_set(context_feature: str | list[str]) -> bool: - """Set context feature if present in context and not in context mask.""" - is_okay: bool = True - context_features: list[str] - if type(context_feature) is str: - context_features = [context_feature] # type: ignore[assignment] - else: - context_features = context_feature # type: ignore[assignment] - for cf in context_features: - if not (cf in context and cf not in context_mask): - is_okay = False - break - return is_okay - mjcf = etree.fromstring(xml_string) default = mjcf.find("./default/") if default is None: default = etree.Element("default") mjcf.addnext(default) - if check_okay_to_set("joint_damping"): - # adjust damping for all joints if damping is already an attribute + # adjust damping for all joints if damping is already an attribute + if "joint_damping" in context: for joint_find in mjcf.findall(".//joint[@damping]"): joint_damping = joint_find.get("damping") joint_find.set( "damping", str(float(joint_damping) * context["joint_damping"]) ) - if check_okay_to_set("joint_stiffness"): - # adjust stiffness for all joints if stiffness is already an attribute + # adjust stiffness for all joints if stiffness is already an attribute + if "joint_stiffness" in context: for joint_find in mjcf.findall(".//joint[@stiffness]"): joint_stiffness = joint_find.get("stiffness") joint_find.set( @@ -53,19 +35,21 @@ def check_okay_to_set(context_feature: str | list[str]) -> bool: if joint is None: joint = etree.Element("joint") default.addnext(joint) - if check_okay_to_set("joint_damping"): + if "joint_damping" in context: def_joint_damping = 0.1 default_joint_damping = str( float(def_joint_damping) * context["joint_damping"] ) joint.set("damping", default_joint_damping) - if check_okay_to_set("joint_stiffness"): + if "joint_stiffness" in context: default_joint_stiffness = str(context["joint_stiffness"]) joint.set("stiffness", default_joint_stiffness) # adjust friction for all geom elements with friction attribute - if check_okay_to_set( - ["friction_tangential", "friction_torsional", "friction_rolling"] + if ( + "friction_tangential" in context + and "friction_torsional" in context + and "friction_rolling" in context ): for geom_find in mjcf.findall(".//geom[@friction]"): friction = geom_find.get("friction").split(" ") @@ -80,18 +64,11 @@ def check_okay_to_set(context_feature: str | list[str]) -> bool: ], ) ): - if ( - (i == 0 and "friction_tangential" not in context_mask) - or (i == 1 and "friction_torsional" not in context_mask) - or (i == 2 and "friction_rolling" not in context_mask) - ): - frict_str += str(float(f) * d) + " " - else: - frict_str += str(f) + " " + frict_str += str(float(f) * d) + " " geom_find.set("friction", frict_str[:-1]) - if check_okay_to_set("geom_density"): - # adjust density for all geom elements with density attribute + # adjust density for all geom elements with density attribute + if "geom_density" in context: for geom_find in mjcf.findall(".//geom[@density]"): geom_find.set( "density", @@ -105,46 +82,37 @@ def check_okay_to_set(context_feature: str | list[str]) -> bool: default.addnext(geom) # set default friction - if geom.get("friction") is None and check_okay_to_set( - ["friction_tangential", "friction_torsional", "friction_rolling"] + if ( + "friction_tangential" in context + and "friction_torsional" in context + and "friction_rolling" in context ): - default_friction_tangential = 1.0 - default_friction_torsional = 0.005 - default_friction_rolling = 0.0001 - geom.set( - "friction", - " ".join( - [ - ( + if geom.get("friction") is None: + default_friction_tangential = 1.0 + default_friction_torsional = 0.005 + default_friction_rolling = 0.0001 + geom.set( + "friction", + " ".join( + [ str( default_friction_tangential * context["friction_tangential"] - ) - if "friction_tangential" not in context_mask - else str(default_friction_tangential) - ), - ( - str(default_friction_torsional * context["friction_torsional"]) - if "friction_torsional" not in context_mask - else str(default_friction_torsional) - ), - ( - str(default_friction_rolling * context["friction_rolling"]) - if "friction_rolling" not in context_mask - else str(default_friction_rolling) - ), - ] - ), - ) + ), + str(default_friction_torsional * context["friction_torsional"]), + str(default_friction_rolling * context["friction_rolling"]), + ] + ), + ) - if check_okay_to_set("geom_density"): - # set default density + # set default density + if "geom_density" in context: geom_density = geom.get("density") if geom_density is None: geom_density = 1000 geom.set("density", str(float(geom_density) * context["geom_density"])) - if check_okay_to_set("actuator_strength"): - # scale all actuators with the actuator strength factor + # scale all actuators with the actuator strength factor + if "actuator_strength" in context: actuators = mjcf.findall("./actuator/") for actuator in actuators: gear = actuator.get("gear") @@ -158,43 +126,39 @@ def check_okay_to_set(context_feature: str | list[str]) -> bool: option = etree.Element("option") mjcf.append(option) - if check_okay_to_set("gravity"): + if "gravity" in context: gravity = option.get("gravity") if gravity is not None: g = gravity.split(" ") - gravity = " ".join([g[0], g[1], str(context["gravity"])]) + gravity = " ".join([g[0], g[1], str(-context["gravity"])]) else: - gravity = " ".join(["0", "0", str(context["gravity"])]) + gravity = " ".join(["0", "0", str(-context["gravity"])]) option.set("gravity", gravity) - if check_okay_to_set("wind"): + if "wind_x" in context and "wind_y" in context and "wind_z" in context: wind = option.get("wind") if wind is not None: - w = wind.split(" ") wind = " ".join( [ - (str(context["wind_x"]) if "wind_x" not in context_mask else w[0]), - (str(context["wind_y"]) if "wind_y" not in context_mask else w[1]), - (str(context["wind_z"]) if "wind_z" not in context_mask else w[2]), + str(context["wind_x"]), + str(context["wind_y"]), + str(context["wind_z"]), ] ) else: wind = " ".join( [ - (str(context["wind_x"]) if "wind_x" not in context_mask else "0"), - (str(context["wind_y"]) if "wind_y" not in context_mask else "0"), - (str(context["wind_z"]) if "wind_z" not in context_mask else "0"), + str(context["wind_x"]), + str(context["wind_y"]), + str(context["wind_z"]), ] ) option.set("wind", wind) - - if check_okay_to_set("timestep"): + if "timestep" in context: option.set("timestep", str(context["timestep"])) - - if check_okay_to_set("density"): + if "density" in context: option.set("density", str(context["density"])) - - if check_okay_to_set("viscosity"): + if "viscosity" in context: option.set("viscosity", str(context["viscosity"])) xml_string = etree.tostring(mjcf, pretty_print=True) diff --git a/carl/envs/dmc/dmc_tasks/walker.py b/carl/envs/dmc/dmc_tasks/walker.py index a0d80628..670a212c 100644 --- a/carl/envs/dmc/dmc_tasks/walker.py +++ b/carl/envs/dmc/dmc_tasks/walker.py @@ -15,7 +15,7 @@ """Planar Walker Domain.""" -from typing import Dict, List, Optional, Tuple, Union +from typing import Dict, Optional, Tuple, Union import collections @@ -54,7 +54,6 @@ def get_model_and_assets() -> Tuple[bytes, Dict]: @SUITE.add("benchmarking") # type: ignore def stand_context( context: Context = {}, - context_mask: List = [], time_limit: int = _DEFAULT_TIME_LIMIT, random: Union[np.random.RandomState, int, None] = None, environment_kwargs: Optional[Dict] = None, @@ -62,9 +61,7 @@ def stand_context( """Returns the Stand task with the adapted context.""" xml_string, assets = get_model_and_assets() if context != {}: - xml_string = adapt_context( - xml_string=xml_string, context=context, context_mask=context_mask - ) + xml_string = adapt_context(xml_string=xml_string, context=context) physics = Physics.from_xml_string(xml_string, assets) task = PlanarWalker(move_speed=0, random=random) environment_kwargs = environment_kwargs or {} @@ -80,7 +77,6 @@ def stand_context( @SUITE.add("benchmarking") # type: ignore def walk_context( context: Context = {}, - context_mask: List = [], time_limit: int = _DEFAULT_TIME_LIMIT, random: Union[np.random.RandomState, int, None] = None, environment_kwargs: Optional[Dict] = None, @@ -88,9 +84,7 @@ def walk_context( """Returns the Walk task with the adapted context.""" xml_string, assets = get_model_and_assets() if context != {}: - xml_string = adapt_context( - xml_string=xml_string, context=context, context_mask=context_mask - ) + xml_string = adapt_context(xml_string=xml_string, context=context) physics = Physics.from_xml_string(xml_string, assets) task = PlanarWalker(move_speed=_WALK_SPEED, random=random) environment_kwargs = environment_kwargs or {} @@ -106,7 +100,6 @@ def walk_context( @SUITE.add("benchmarking") # type: ignore def run_context( context: Context = {}, - context_mask: List = [], time_limit: int = _DEFAULT_TIME_LIMIT, random: Union[np.random.RandomState, int, None] = None, environment_kwargs: Optional[Dict] = None, @@ -114,9 +107,7 @@ def run_context( """Returns the Run task with the adapted context.""" xml_string, assets = get_model_and_assets() if context != {}: - xml_string = adapt_context( - xml_string=xml_string, context=context, context_mask=context_mask - ) + xml_string = adapt_context(xml_string=xml_string, context=context) physics = Physics.from_xml_string(xml_string, assets) task = PlanarWalker(move_speed=_RUN_SPEED, random=random) environment_kwargs = environment_kwargs or {} diff --git a/carl/envs/dmc/loader.py b/carl/envs/dmc/loader.py index 30c41738..0633fabd 100644 --- a/carl/envs/dmc/loader.py +++ b/carl/envs/dmc/loader.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, List, Optional +from typing import Any, Dict, Optional import inspect @@ -24,12 +24,10 @@ def load_dmc_env( domain_name: str, task_name: str, context: Context = {}, - context_mask: Optional[List[str]] = [], task_kwargs: Optional[Any] = None, environment_kwargs: Dict[str, bool] = None, visualize_reward: bool = False, ) -> dm_env: - if domain_name in _DOMAINS: domain = _DOMAINS[domain_name] elif domain_name in suite._DOMAINS: @@ -41,9 +39,7 @@ def load_dmc_env( task_kwargs = task_kwargs or {} if environment_kwargs is not None: task_kwargs = dict(task_kwargs, environment_kwargs=environment_kwargs) - env = domain.SUITE[task_name]( - context=context, context_mask=context_mask, **task_kwargs - ) + env = domain.SUITE[task_name](context=context, **task_kwargs) env.task.visualize_reward = visualize_reward return env elif (domain_name, task_name) in suite.ALL_TASKS: diff --git a/carl/envs/dmc/wrappers.py b/carl/envs/dmc/wrappers.py index 1a9b203f..7ac9a059 100644 --- a/carl/envs/dmc/wrappers.py +++ b/carl/envs/dmc/wrappers.py @@ -66,7 +66,8 @@ def step(self, action: ActType) -> Tuple[ObsType, float, bool, dict]: Returns: observation (object): agent's observation of the current environment reward (float) : amount of reward returned after previous action - done (bool): whether the episode has ended, in which case further step() calls will return undefined results + terminated (bool): whether termination condition is reached + truncated (bool): whether the episode has ended due to time limit info (dict): contains auxiliary diagnostic information (helpful for debugging, logging, and sometimes learning) """ @@ -77,7 +78,7 @@ def step(self, action: ActType) -> Tuple[ObsType, float, bool, dict]: observation = timestep.observation["observations"] info = {"step_type": step_type, "discount": discount} done = step_type == StepType.LAST - return observation, reward, done, info + return observation, reward, False, done, info def reset( self, @@ -86,15 +87,13 @@ def reset( return_info: bool = False, options: Optional[dict] = None, ) -> Union[ObsType, tuple[ObsType, dict]]: - super(MujocoToGymWrapper, self).reset( - seed=seed, return_info=return_info, options=options - ) + super(MujocoToGymWrapper, self).reset(seed=seed, options=options) timestep = self.env.reset() if isinstance(self.observation_space, spaces.Box): observation = timestep.observation["observations"] else: raise NotImplementedError - return observation + return observation, {} def render( self, mode: str = "human", camera_id: int = 0, **kwargs: Any diff --git a/carl/envs/gymnasium/__init__.py b/carl/envs/gymnasium/__init__.py new file mode 100644 index 00000000..62ba5092 --- /dev/null +++ b/carl/envs/gymnasium/__init__.py @@ -0,0 +1,15 @@ +from carl.envs.gymnasium.classic_control import ( + CARLAcrobot, + CARLCartPole, + CARLMountainCar, + CARLMountainCarContinuous, + CARLPendulum, +) + +__all__ = [ + "CARLAcrobot", + "CARLCartPole", + "CARLMountainCar", + "CARLMountainCarContinuous", + "CARLPendulum", +] diff --git a/carl/envs/gymnasium/box2d/__init__.py b/carl/envs/gymnasium/box2d/__init__.py new file mode 100644 index 00000000..65ddd19e --- /dev/null +++ b/carl/envs/gymnasium/box2d/__init__.py @@ -0,0 +1,6 @@ +# flake8: noqa: F401 +from carl.envs.gymnasium.box2d.carl_bipedal_walker import CARLBipedalWalker +from carl.envs.gymnasium.box2d.carl_lunarlander import CARLLunarLander +from carl.envs.gymnasium.box2d.carl_vehicle_racing import CARLVehicleRacing + +__all__ = ["CARLBipedalWalker", "CARLLunarLander", "CARLVehicleRacing"] diff --git a/carl/envs/box2d/carl_bipedal_walker.py b/carl/envs/gymnasium/box2d/carl_bipedal_walker.py similarity index 55% rename from carl/envs/box2d/carl_bipedal_walker.py rename to carl/envs/gymnasium/box2d/carl_bipedal_walker.py index 33e66453..197b1bff 100644 --- a/carl/envs/box2d/carl_bipedal_walker.py +++ b/carl/envs/gymnasium/box2d/carl_bipedal_walker.py @@ -1,131 +1,89 @@ -from typing import Dict, List, Optional, Union +from __future__ import annotations import numpy as np from Box2D.b2 import edgeShape, fixtureDef, polygonShape -from gym.envs.box2d import bipedal_walker -from gym.envs.box2d import bipedal_walker as bpw +from gymnasium.envs.box2d import bipedal_walker +from gymnasium.envs.box2d import bipedal_walker as bpw -from carl.context.selection import AbstractSelector -from carl.envs.carl_env import CARLEnv -from carl.utils.trial_logger import TrialLogger -from carl.utils.types import Context, Contexts +from carl.context.context_space import ( + ContextFeature, + UniformFloatContextFeature, + UniformIntegerContextFeature, +) +from carl.envs.gymnasium.carl_gymnasium_env import CARLGymnasiumEnv -DEFAULT_CONTEXT = { - "FPS": 50, - "SCALE": 30.0, # affects how fast-paced the game is, forces should be adjusted as well - "GRAVITY_X": 0, - "GRAVITY_Y": -10, - # surroundings - "FRICTION": 2.5, - "TERRAIN_STEP": 14 / 30.0, - "TERRAIN_LENGTH": 200, # in steps - "TERRAIN_HEIGHT": 600 / 30 / 4, # VIEWPORT_H/SCALE/4 - "TERRAIN_GRASS": 10, # low long are grass spots, in steps - "TERRAIN_STARTPAD": 20, # in steps - # walker - "MOTORS_TORQUE": 80, - "SPEED_HIP": 4, - "SPEED_KNEE": 6, - "LIDAR_RANGE": 160 / 30.0, - "LEG_DOWN": -8 / 30.0, - "LEG_W": 8 / 30.0, - "LEG_H": 34 / 30.0, - # absolute value of random force applied to walker at start of episode - "INITIAL_RANDOM": 5, - # Size of world - "VIEWPORT_W": 600, - "VIEWPORT_H": 400, -} -# TODO make bounds more generous for all Box2D envs? -CONTEXT_BOUNDS = { - "FPS": (1, 500, float), - "SCALE": ( - 1, - 100, - float, - ), # affects how fast-paced the game is, forces should be adjusted as well - # surroundings - "FRICTION": (0, 10, float), - "TERRAIN_STEP": (0.25, 1, float), - "TERRAIN_LENGTH": (100, 500, int), # in steps - "TERRAIN_HEIGHT": (3, 10, float), # VIEWPORT_H/SCALE/4 - "TERRAIN_GRASS": (5, 15, int), # low long are grass spots, in steps - "TERRAIN_STARTPAD": (10, 30, int), # in steps - # walker - "MOTORS_TORQUE": (0, 200, float), - "SPEED_HIP": (1e-6, 15, float), - "SPEED_KNEE": (1e-6, 15, float), - "LIDAR_RANGE": (0.5, 20, float), - "LEG_DOWN": (-2, -0.25, float), - "LEG_W": (0.25, 0.5, float), - "LEG_H": (0.25, 2, float), - # absolute value of random force applied to walker at start of episode - "INITIAL_RANDOM": (0, 50, float), - # Size of world - "VIEWPORT_W": (400, 1000, int), - "VIEWPORT_H": (200, 800, int), - "GRAVITY_X": (-20, 20, float), # unit: m/s² - "GRAVITY_Y": ( - -20, - -0.01, - float, - ), # the y-component of gravity must be smaller than 0 because otherwise the - # body leaves the frame by going up -} +class CARLBipedalWalker(CARLGymnasiumEnv): + env_name: str = "BipedalWalker-v3" - -class CARLBipedalWalkerEnv(CARLEnv): - def __init__( - self, - env: Optional[bipedal_walker.BipedalWalker] = None, - contexts: Contexts = {}, - hide_context: bool = True, - add_gaussian_noise_to_context: bool = False, - gaussian_noise_std_percentage: float = 0.05, - logger: Optional[TrialLogger] = None, - scale_context_features: str = "no", - default_context: Optional[Context] = DEFAULT_CONTEXT, - state_context_features: Optional[List[str]] = None, - context_mask: Optional[List[str]] = None, - dict_observation_space: bool = False, - context_selector: Optional[ - Union[AbstractSelector, type[AbstractSelector]] - ] = None, - context_selector_kwargs: Optional[Dict] = None, - ): - """ - - Parameters - ---------- - env: gym.Env, optional - Defaults to classic control environment mountain car from gym (MountainCarEnv). - contexts: List[Dict], optional - Different contexts / different environment parameter settings. - instance_mode: str, optional - """ - if env is None: - env = bipedal_walker.BipedalWalker() - if not contexts: - contexts = {0: DEFAULT_CONTEXT} - super().__init__( - env=env, - contexts=contexts, - hide_context=hide_context, - add_gaussian_noise_to_context=add_gaussian_noise_to_context, - gaussian_noise_std_percentage=gaussian_noise_std_percentage, - logger=logger, - scale_context_features=scale_context_features, - default_context=default_context, - state_context_features=state_context_features, - dict_observation_space=dict_observation_space, - context_selector=context_selector, - context_selector_kwargs=context_selector_kwargs, - context_mask=context_mask, - ) - self.whitelist_gaussian_noise = list( - DEFAULT_CONTEXT.keys() - ) # allow to augment all values + @staticmethod + def get_context_features() -> dict[str, ContextFeature]: + return { + "FPS": UniformFloatContextFeature( + "FPS", lower=1, upper=500, default_value=50 + ), + "SCALE": UniformFloatContextFeature( + "SCALE", lower=1, upper=100, default_value=30.0 + ), # affects how fast-paced the game is, forces should be adjusted as well + "GRAVITY_X": UniformFloatContextFeature( + "GRAVITY_X", lower=-20, upper=20, default_value=0 + ), + "GRAVITY_Y": UniformFloatContextFeature( + "GRAVITY_Y", lower=-20, upper=-0.01, default_value=-10 + ), + # surroundings + "FRICTION": UniformFloatContextFeature( + "FRICTION", lower=0, upper=10, default_value=2.5 + ), + "TERRAIN_STEP": UniformFloatContextFeature( + "TERRAIN_STEP", lower=0.25, upper=1, default_value=14 / 30.0 + ), + "TERRAIN_LENGTH": UniformIntegerContextFeature( + "TERRAIN_LENGTH", lower=100, upper=500, default_value=200 + ), # in steps + "TERRAIN_HEIGHT": UniformFloatContextFeature( + "TERRAIN_HEIGHT", lower=3, upper=10, default_value=600 / 30 / 4 + ), # VIEWPORT_H/SCALE/4 + "TERRAIN_GRASS": UniformIntegerContextFeature( + "TERRAIN_GRASS", lower=5, upper=15, default_value=10 + ), # low long are grass spots, in step)s + "TERRAIN_STARTPAD": UniformFloatContextFeature( + "TERRAIN_STARTPAD", lower=10, upper=30, default_value=20 + ), # in steps + # walker + "MOTORS_TORQUE": UniformFloatContextFeature( + "MOTORS_TORQUE", lower=0.01, upper=200, default_value=80 + ), + "SPEED_HIP": UniformFloatContextFeature( + "SPEED_HIP", lower=0.01, upper=15, default_value=4 + ), + "SPEED_KNEE": UniformFloatContextFeature( + "SPEED_KNEE", lower=0.01, upper=15, default_value=6 + ), + "LIDAR_RANGE": UniformFloatContextFeature( + "LIDAR_RANGE", lower=0.01, upper=20, default_value=160 / 30.0 + ), + "LEG_DOWN": UniformFloatContextFeature( + "LEG_DOWN", lower=-2, upper=-0.25, default_value=-8 / 30.0 + ), + "LEG_W": UniformFloatContextFeature( + "LEG_W", lower=0.25, upper=0.5, default_value=8 / 30.0 + ), + "LEG_H": UniformFloatContextFeature( + "LEG_H", lower=0.25, upper=2, default_value=34 / 30.0 + ), + # absolute value of random force applied to walker at start of episode + "INITIAL_RANDOM": UniformFloatContextFeature( + "INITIAL_RANDOM", lower=0, upper=50, default_value=5 + ), + # Size of world + "VIEWPORT_W": UniformIntegerContextFeature( + "VIEWPORT_W", lower=400, upper=1000, default_value=600 + ), + "VIEWPORT_H": UniformIntegerContextFeature( + "VIEWPORT_H", lower=200, upper=800, default_value=400 + ), + } def _update_context(self) -> None: self.env: bipedal_walker.BipedalWalker @@ -197,9 +155,7 @@ def _update_context(self) -> None: self.env.world.gravity = gravity -def demo_heuristic( - env: Union[CARLBipedalWalkerEnv, bipedal_walker.BipedalWalker] -) -> None: +def demo_heuristic(env: CARLBipedalWalker | bipedal_walker.BipedalWalker) -> None: env.reset() steps = 0 total_reward = 0 @@ -212,9 +168,10 @@ def demo_heuristic( SUPPORT_KNEE_ANGLE = +0.1 supporting_knee_angle = SUPPORT_KNEE_ANGLE while True: - s, r, done, info = env.step(a) + s, r, terminated, truncated, info = env.step(a) + s = s["state"] total_reward += r - if steps % 20 == 0 or done: + if steps % 20 == 0 or terminated or truncated: print("\naction " + str(["{:+0.2f}".format(x) for x in a])) print("step {} total_reward {:+0.2f}".format(steps, total_reward)) print("hull " + str(["{:+0.2f}".format(x) for x in s[0:4]])) @@ -278,13 +235,12 @@ def demo_heuristic( a = np.clip(0.5 * a, -1.0, 1.0) env.render() - if done: + if terminated or truncated: break if __name__ == "__main__": # Heurisic: suboptimal, have no notion of balance. - env = CARLBipedalWalkerEnv(add_gaussian_noise_to_context=True) - for i in range(3): - demo_heuristic(env) + env = CARLBipedalWalker() + demo_heuristic(env) env.close() diff --git a/carl/envs/gymnasium/box2d/carl_lunarlander.py b/carl/envs/gymnasium/box2d/carl_lunarlander.py new file mode 100644 index 00000000..1800f7f9 --- /dev/null +++ b/carl/envs/gymnasium/box2d/carl_lunarlander.py @@ -0,0 +1,84 @@ +from __future__ import annotations + +from gymnasium.envs.box2d import lunar_lander +from gymnasium.envs.box2d.lunar_lander import LunarLander + +from carl.context.context_space import ( + ContextFeature, + UniformFloatContextFeature, + UniformIntegerContextFeature, +) +from carl.envs.gymnasium.carl_gymnasium_env import CARLGymnasiumEnv + + +class CARLLunarLander(CARLGymnasiumEnv): + env_name: str = "LunarLander-v2" + + @staticmethod + def get_context_features() -> dict[str, ContextFeature]: + return { + "FPS": UniformFloatContextFeature( + "FPS", lower=1, upper=500, default_value=50 + ), + "SCALE": UniformFloatContextFeature( + "SCALE", lower=1, upper=100, default_value=30.0 + ), # affects how fast-paced the game is, forces should be adjusted as well + # Engine powers + "MAIN_ENGINE_POWER": UniformFloatContextFeature( + "MAIN_ENGINE_POWER", lower=0, upper=50, default_value=13.0 + ), + "SIDE_ENGINE_POWER": UniformFloatContextFeature( + "SIDE_ENGINE_POWER", lower=0, upper=50, default_value=0.6 + ), + # random force on lunar lander body on reset + "INITIAL_RANDOM": UniformFloatContextFeature( + "INITIAL_RANDOM", lower=0, upper=2000, default_value=1000.0 + ), # Set 1500 to make game harder + "GRAVITY_X": UniformFloatContextFeature( + "GRAVITY_X", lower=-20, upper=20, default_value=0 + ), + "GRAVITY_Y": UniformFloatContextFeature( + "GRAVITY_Y", lower=-20, upper=0.01, default_value=-10 + ), + # lunar lander body specification + "LEG_AWAY": UniformFloatContextFeature( + "LEG_AWAY", lower=0, upper=50, default_value=20 + ), + "LEG_DOWN": UniformFloatContextFeature( + "LEG_DOWN", lower=0, upper=50, default_value=18 + ), + "LEG_W": UniformFloatContextFeature( + "LEG_W", lower=1, upper=10, default_value=2 + ), + "LEG_H": UniformFloatContextFeature( + "LEG_H", lower=1, upper=20, default_value=8 + ), + "LEG_SPRING_TORQUE": UniformFloatContextFeature( + "LEG_SPRING_TORQUE", lower=0, upper=100, default_value=40 + ), + "SIDE_ENGINE_HEIGHT": UniformFloatContextFeature( + "SIDE_ENGINE_HEIGHT", lower=1, upper=20, default_value=14.0 + ), + "SIDE_ENGINE_AWAY": UniformFloatContextFeature( + "SIDE_ENGINE_AWAY", lower=1, upper=20, default_value=12.0 + ), + # Size of worl)d + "VIEWPORT_W": UniformIntegerContextFeature( + "VIEWPORT_W", lower=400, upper=1000, default_value=600 + ), + "VIEWPORT_H": UniformIntegerContextFeature( + "VIEWPORT_H", lower=200, upper=800, default_value=400 + ), + } + + def _update_context(self) -> None: + self.env: LunarLander + for key, value in self.context.items(): + if hasattr(lunar_lander, key): + setattr(lunar_lander, key, value) + + gravity_x = self.context["GRAVITY_X"] + gravity_y = self.context["GRAVITY_Y"] + + gravity = (gravity_x, gravity_y) + self.env.world.gravity = gravity diff --git a/carl/envs/gymnasium/box2d/carl_vehicle_racing.py b/carl/envs/gymnasium/box2d/carl_vehicle_racing.py new file mode 100644 index 00000000..8558531f --- /dev/null +++ b/carl/envs/gymnasium/box2d/carl_vehicle_racing.py @@ -0,0 +1,243 @@ +from __future__ import annotations + +from typing import Any, List, Optional, Tuple, Type, Union + +import numpy as np +from gymnasium.envs.box2d.car_dynamics import Car +from gymnasium.envs.box2d.car_racing import CarRacing +from gymnasium.envs.registration import register + +from carl.context.context_space import ContextFeature, UniformIntegerContextFeature +from carl.envs.gymnasium.box2d.parking_garage.bus import AWDBus # as Car +from carl.envs.gymnasium.box2d.parking_garage.bus import AWDBusLargeTrailer # as Car +from carl.envs.gymnasium.box2d.parking_garage.bus import AWDBusSmallTrailer # as Car +from carl.envs.gymnasium.box2d.parking_garage.bus import Bus # as Car +from carl.envs.gymnasium.box2d.parking_garage.bus import BusLargeTrailer # as Car +from carl.envs.gymnasium.box2d.parking_garage.bus import BusSmallTrailer # as Car +from carl.envs.gymnasium.box2d.parking_garage.bus import FWDBus # as Car +from carl.envs.gymnasium.box2d.parking_garage.bus import FWDBusLargeTrailer # as Car +from carl.envs.gymnasium.box2d.parking_garage.bus import FWDBusSmallTrailer # as Car +from carl.envs.gymnasium.box2d.parking_garage.race_car import AWDRaceCar # as Car +from carl.envs.gymnasium.box2d.parking_garage.race_car import FWDRaceCar # as Car +from carl.envs.gymnasium.box2d.parking_garage.race_car import ( # as Car + AWDRaceCarLargeTrailer, + AWDRaceCarSmallTrailer, + FWDRaceCarLargeTrailer, + FWDRaceCarSmallTrailer, + RaceCar, + RaceCarLargeTrailer, + RaceCarSmallTrailer, +) +from carl.envs.gymnasium.box2d.parking_garage.street_car import AWDStreetCar # as Car +from carl.envs.gymnasium.box2d.parking_garage.street_car import FWDStreetCar # as Car +from carl.envs.gymnasium.box2d.parking_garage.street_car import StreetCar # as Car +from carl.envs.gymnasium.box2d.parking_garage.street_car import ( # as Car + AWDStreetCarLargeTrailer, + AWDStreetCarSmallTrailer, + FWDStreetCarLargeTrailer, + FWDStreetCarSmallTrailer, + StreetCarLargeTrailer, + StreetCarSmallTrailer, +) +from carl.envs.gymnasium.box2d.parking_garage.trike import TukTuk # as Car +from carl.envs.gymnasium.box2d.parking_garage.trike import TukTukSmallTrailer # as Car +from carl.envs.gymnasium.carl_gymnasium_env import CARLGymnasiumEnv +from carl.utils.types import ObsType + +PARKING_GARAGE_DICT = { + # Racing car + "RaceCar": RaceCar, + "FWDRaceCar": FWDRaceCar, + "AWDRaceCar": AWDRaceCar, + "RaceCarSmallTrailer": RaceCarSmallTrailer, + "FWDRaceCarSmallTrailer": FWDRaceCarSmallTrailer, + "AWDRaceCarSmallTrailer": AWDRaceCarSmallTrailer, + "RaceCarLargeTrailer": RaceCarLargeTrailer, + "FWDRaceCarLargeTrailer": FWDRaceCarLargeTrailer, + "AWDRaceCarLargeTrailer": AWDRaceCarLargeTrailer, + # Street car + "StreetCar": StreetCar, + "FWDStreetCar": FWDStreetCar, + "AWDStreetCar": AWDStreetCar, + "StreetCarSmallTrailer": StreetCarSmallTrailer, + "FWDStreetCarSmallTrailer": FWDStreetCarSmallTrailer, + "AWDStreetCarSmallTrailer": AWDStreetCarSmallTrailer, + "StreetCarLargeTrailer": StreetCarLargeTrailer, + "FWDStreetCarLargeTrailer": FWDStreetCarLargeTrailer, + "AWDStreetCarLargeTrailer": AWDStreetCarLargeTrailer, + # Bus + "Bus": Bus, + "FWDBus": FWDBus, + "AWDBus": AWDBus, + "BusSmallTrailer": BusSmallTrailer, + "FWDBusSmallTrailer": FWDBusSmallTrailer, + "AWDBusSmallTrailer": AWDBusSmallTrailer, + "BusLargeTrailer": BusLargeTrailer, + "FWDBusLargeTrailer": FWDBusLargeTrailer, + "AWDBusLargeTrailer": AWDBusLargeTrailer, + # Tuk Tuk :) + "TukTuk": TukTuk, + "TukTukSmallTrailer": TukTukSmallTrailer, +} +PARKING_GARAGE = list(PARKING_GARAGE_DICT.values()) +VEHICLE_NAMES = list(PARKING_GARAGE_DICT.keys()) +DEFAULT_CONTEXT = { + "VEHICLE": PARKING_GARAGE.index(RaceCar), +} + +CONTEXT_BOUNDS = { + "VEHICLE": (None, None, "categorical", np.arange(0, len(PARKING_GARAGE))) +} +CATEGORICAL_CONTEXT_FEATURES = ["VEHICLE"] + + +class CustomCarRacing(CarRacing): + def __init__( + self, + vehicle_class: Type[Car] = Car, + verbose: bool = True, + render_mode: Optional[str] = None, + ): + super().__init__(verbose=verbose, render_mode=render_mode) + self.vehicle_class = vehicle_class + + def reset( + self, + *, + seed: Optional[int] = None, + return_info: bool = True, + options: Optional[dict] = None, + ) -> Union[ObsType, tuple[ObsType, dict]]: + self._destroy() + self.reward = 0.0 + self.prev_reward = 0.0 + self.tile_visited_count = 0 + self.t = 0.0 + self.road_poly: List[Tuple[List[float], Tuple[Any]]] = [] + + while True: + success = self._create_track() + if success: + break + if self.verbose == 1: + print( + "retry to generate track (normal if there are not many" + "instances of this message)" + ) + self.car = self.vehicle_class(self.world, *self.track[0][1:4]) # type: ignore [assignment] + + for i in range( + 49 + ): # this sets up the environment and resolves any initial violations of geometry + self.step(None) # type: ignore [arg-type] + + return self.step(None)[0], {} + + def _render_indicators_BROKEN(self, W: int, H: int) -> None: + # TODO Fix CarRacing rendering + # copied from meta car racing + s = W / 40.0 + h = H / 40.0 + colors = [0, 0, 0, 1] * 4 + polygons = [W, 0, 0, W, 5 * h, 0, 0, 5 * h, 0, 0, 0, 0] + + def vertical_ind(place: int, val: int, color: Tuple) -> None: + points = [ + place * s, + h + h * val, + 0, + (place + 1) * s, + h + h * val, + 0, + (place + 1) * s, + h, + 0, + (place + 0) * s, + h, + 0, + ] + C = [color[0], color[1], color[2], 1] # * 4 + colors.extend(C) + polygons.extend(points) + self._draw_colored_polygon( + self.surf, points, C, zoom=1, translation=[0, 0], angle=0, clip=True + ) + + def horiz_ind(place: int, val: int, color: Tuple) -> None: + points = [ + (place + 0) * s, + 4 * h, + 0, + (place + val) * s, + 4 * h, + 0, + (place + val) * s, + 2 * h, + 0, + (place + 0) * s, + 2 * h, + 0, + ] + C = [color[0], color[1], color[2], 1] # * 4 + colors.extend(C) + polygons.extend(points) + self._draw_colored_polygon( + self.surf, points, C, zoom=1, translation=[0, 0], angle=0, clip=True + ) + + true_speed = np.sqrt( + np.square(self.car.hull.linearVelocity[0]) # type: ignore [attr-defined] + + np.square(self.car.hull.linearVelocity[1]) # type: ignore [attr-defined] + ) + + vertical_ind(5, 0.02 * true_speed, (1, 1, 1)) + + # Custom render to handle different amounts of wheels + vertical_ind(7, 0.01 * self.car.wheels[0].omega, (0.0, 0, 1)) # type: ignore [attr-defined] + for i in range(len(self.car.wheels)): # type: ignore [attr-defined] + vertical_ind(7 + i, 0.01 * self.car.wheels[i].omega, (0.0 + i * 0.1, 0, 1)) # type: ignore [attr-defined] + horiz_ind(20, -10.0 * self.car.wheels[0].joint.angle, (0, 1, 0)) # type: ignore [attr-defined] + horiz_ind(30, -0.8 * self.car.hull.angularVelocity, (1, 0, 0)) # type: ignore [attr-defined] + # vl = pyglet.graphics.vertex_list( + # len(polygons) // 3, ("v3f", polygons), ("c4f", colors) # gl.GL_QUADS, + # ) + # vl.draw(gl.GL_QUADS) + + # shader_program = pyglet.graphics.get_default_shader() + + # mode = gl.GL_POLYGON_MODE + + # vertex_positions = polygons + # vl = shader_program.vertex_list( + # count=len(polygons) // 3, + # mode=mode, + # position=('f', vertex_positions), + # colors=('f', colors) + # ) + # vl.draw(mode) + + +register( + id="CustomCarRacing-v2", + entry_point="carl.envs.gymnasium.box2d.carl_vehicle_racing:CustomCarRacing", + max_episode_steps=1000, + reward_threshold=900, +) + + +class CARLVehicleRacing(CARLGymnasiumEnv): + env_name: str = "CustomCarRacing-v2" + + @staticmethod + def get_context_features() -> dict[str, ContextFeature]: + return { + "VEHICLE_ID": UniformIntegerContextFeature( + "VEHICLE_ID", lower=0, upper=len(PARKING_GARAGE) - 1, default_value=0 + ) # RaceCar + } + + def _update_context(self) -> None: + self.env: CustomCarRacing + vehicle_class_index = self.context["VEHICLE_ID"] + self.env.vehicle_class = PARKING_GARAGE[vehicle_class_index] + print(self.env.vehicle_class) diff --git a/carl/envs/box2d/parking_garage/__init__.py b/carl/envs/gymnasium/box2d/parking_garage/__init__.py similarity index 100% rename from carl/envs/box2d/parking_garage/__init__.py rename to carl/envs/gymnasium/box2d/parking_garage/__init__.py diff --git a/carl/envs/box2d/parking_garage/bus.py b/carl/envs/gymnasium/box2d/parking_garage/bus.py similarity index 98% rename from carl/envs/box2d/parking_garage/bus.py rename to carl/envs/gymnasium/box2d/parking_garage/bus.py index 7d6e810e..6d7c5ffa 100644 --- a/carl/envs/box2d/parking_garage/bus.py +++ b/carl/envs/gymnasium/box2d/parking_garage/bus.py @@ -12,9 +12,9 @@ from Box2D.b2 import revoluteJointDef # noqa: F401 from Box2D.b2 import ropeJointDef # noqa: F401 from Box2D.b2 import shape # noqa: F401; noqa: F401 -from gym.envs.box2d.car_dynamics import Car +from gymnasium.envs.box2d.car_dynamics import Car -from carl.envs.box2d.parking_garage.utils import Particle +from carl.envs.gymnasium.box2d.parking_garage.utils import Particle __author__ = "André Biedenkapp" @@ -379,7 +379,8 @@ def brake(self, b: float) -> None: """control: brake Args: - b (0..1): Degree to which the brakes are applied. More than 0.9 blocks the wheels to zero rotation""" + b (0..1): Degree to which the brakes are applied. More than 0.9 blocks the wheels to zero rotation + """ for w in self.wheels[:2]: w.brake = b * 0.4 for w in self.wheels[2:4]: @@ -395,7 +396,8 @@ def steer(self, s: float) -> None: """control: steer Args: - s (-1..1): target position, it takes time to rotate steering wheel from side-to-side""" + s (-1..1): target position, it takes time to rotate steering wheel from side-to-side + """ self.wheels[0].steer = s self.wheels[1].steer = s diff --git a/carl/envs/box2d/parking_garage/race_car.py b/carl/envs/gymnasium/box2d/parking_garage/race_car.py similarity index 99% rename from carl/envs/box2d/parking_garage/race_car.py rename to carl/envs/gymnasium/box2d/parking_garage/race_car.py index c82c3e58..acd2fcf4 100644 --- a/carl/envs/box2d/parking_garage/race_car.py +++ b/carl/envs/gymnasium/box2d/parking_garage/race_car.py @@ -12,9 +12,9 @@ from Box2D.b2 import revoluteJointDef # noqa: F401 from Box2D.b2 import ropeJointDef # noqa: F401 from Box2D.b2 import shape # noqa: F401; noqa: F401 -from gym.envs.box2d.car_dynamics import Car +from gymnasium.envs.box2d.car_dynamics import Car -from carl.envs.box2d.parking_garage.utils import Particle +from carl.envs.gymnasium.box2d.parking_garage.utils import Particle __author__ = "André Biedenkapp" @@ -398,7 +398,8 @@ def brake(self, b: float) -> None: """control: brake Args: - b (0..1): Degree to which the brakes are applied. More than 0.9 blocks the wheels to zero rotation""" + b (0..1): Degree to which the brakes are applied. More than 0.9 blocks the wheels to zero rotation + """ for w in self.wheels[:2]: w.brake = b * 0.4 for w in self.wheels[2:4]: @@ -414,7 +415,8 @@ def steer(self, s: float) -> None: """control: steer Args: - s (-1..1): target position, it takes time to rotate steering wheel from side-to-side""" + s (-1..1): target position, it takes time to rotate steering wheel from side-to-side + """ self.wheels[0].steer = s self.wheels[1].steer = s diff --git a/carl/envs/box2d/parking_garage/street_car.py b/carl/envs/gymnasium/box2d/parking_garage/street_car.py similarity index 99% rename from carl/envs/box2d/parking_garage/street_car.py rename to carl/envs/gymnasium/box2d/parking_garage/street_car.py index e609627f..63f291b6 100644 --- a/carl/envs/box2d/parking_garage/street_car.py +++ b/carl/envs/gymnasium/box2d/parking_garage/street_car.py @@ -12,7 +12,7 @@ from Box2D.b2 import revoluteJointDef # noqa: F401 from Box2D.b2 import ropeJointDef # noqa: F401 from Box2D.b2 import shape # noqa: F401; noqa: F401 -from gym.envs.box2d.car_dynamics import Car +from gymnasium.envs.box2d.car_dynamics import Car __author__ = "André Biedenkapp" @@ -389,7 +389,8 @@ def brake(self, b: float) -> None: """control: brake Args: - b (0..1): Degree to which the brakes are applied. More than 0.9 blocks the wheels to zero rotation""" + b (0..1): Degree to which the brakes are applied. More than 0.9 blocks the wheels to zero rotation + """ for w in self.wheels[:2]: w.brake = b * 0.4 for w in self.wheels[2:4]: @@ -405,7 +406,8 @@ def steer(self, s: float) -> None: """control: steer Args: - s (-1..1): target position, it takes time to rotate steering wheel from side-to-side""" + s (-1..1): target position, it takes time to rotate steering wheel from side-to-side + """ self.wheels[0].steer = s self.wheels[1].steer = s diff --git a/carl/envs/box2d/parking_garage/trike.py b/carl/envs/gymnasium/box2d/parking_garage/trike.py similarity index 98% rename from carl/envs/box2d/parking_garage/trike.py rename to carl/envs/gymnasium/box2d/parking_garage/trike.py index d2bcf95a..53df4774 100644 --- a/carl/envs/box2d/parking_garage/trike.py +++ b/carl/envs/gymnasium/box2d/parking_garage/trike.py @@ -12,9 +12,9 @@ from Box2D.b2 import revoluteJointDef # noqa: F401 from Box2D.b2 import ropeJointDef # noqa: F401 from Box2D.b2 import shape # noqa: F401; noqa: F401 -from gym.envs.box2d.car_dynamics import Car +from gymnasium.envs.box2d.car_dynamics import Car -from carl.envs.box2d.parking_garage.utils import Particle +from carl.envs.gymnasium.box2d.parking_garage.utils import Particle __author__ = "André Biedenkapp" @@ -231,7 +231,8 @@ def steer(self, s: float) -> None: """control: steer Args: - s (-1..1): target position, it takes time to rotate steering wheel from side-to-side""" + s (-1..1): target position, it takes time to rotate steering wheel from side-to-side + """ self.wheels[0].steer = s def gas(self, gas: float) -> None: @@ -254,7 +255,8 @@ def brake(self, b: float) -> None: """control: brake Args: - b (0..1): Degree to which the brakes are applied. More than 0.9 blocks the wheels to zero rotation""" + b (0..1): Degree to which the brakes are applied. More than 0.9 blocks the wheels to zero rotation + """ for w in self.wheels[0]: w.brake = b * 10 for w in self.wheels[1:3]: diff --git a/carl/envs/box2d/parking_garage/utils.py b/carl/envs/gymnasium/box2d/parking_garage/utils.py similarity index 100% rename from carl/envs/box2d/parking_garage/utils.py rename to carl/envs/gymnasium/box2d/parking_garage/utils.py diff --git a/carl/envs/gymnasium/carl_gymnasium_env.py b/carl/envs/gymnasium/carl_gymnasium_env.py new file mode 100644 index 00000000..60a49449 --- /dev/null +++ b/carl/envs/gymnasium/carl_gymnasium_env.py @@ -0,0 +1,76 @@ +from __future__ import annotations + +import gymnasium +import pygame +from gymnasium.core import Env + +from carl.context.selection import AbstractSelector +from carl.envs.carl_env import CARLEnv +from carl.utils.types import Contexts + +try: + pygame.display.init() +except: + import os + + os.environ["SDL_VIDEODRIVER"] = "dummy" + + +class CARLGymnasiumEnv(CARLEnv): + env_name: str + render_mode: str = "rgb_array" + + def __init__( + self, + env: Env | None = None, + contexts: Contexts | None = None, + obs_context_features: list[str] + | None = None, # list the context features which should be added to the state + obs_context_as_dict: bool = True, + context_selector: AbstractSelector | type[AbstractSelector] | None = None, + context_selector_kwargs: dict = None, + **kwargs, + ) -> None: + """ + CARL Gymnasium Environment. + + Parameters + ---------- + + env : Env | None + Gymnasium environment, the default is None. + If None, instantiate the env with gymnasium's make function and + `self.env_name` which is defined in each child class. + contexts : Contexts | None, optional + Context set, by default None. If it is None, we build the + context set with the default context. + obs_context_features : list[str] | None, optional + Context features which should be included in the observation, by default None. + If they are None, add all context features. + context_selector: AbstractSelector | type[AbstractSelector] | None, optional + The context selector (class), after each reset selects a new context to use. + If None, use a round robin selector. + context_selector_kwargs : dict, optional + Optional keyword arguments for the context selector, by default None. + Only used when `context_selector` is not None. + + Attributes + ---------- + env_name: str + The registered gymnasium environment name. + """ + if env is None: + env = gymnasium.make(id=self.env_name, render_mode=self.render_mode) + super().__init__( + env=env, + contexts=contexts, + obs_context_features=obs_context_features, + obs_context_as_dict=obs_context_as_dict, + context_selector=context_selector, + context_selector_kwargs=context_selector_kwargs, + **kwargs, + ) + + def _update_context(self) -> None: + for k, v in self.context.items(): + setattr(self.env, k, v) diff --git a/carl/envs/gymnasium/classic_control/__init__.py b/carl/envs/gymnasium/classic_control/__init__.py new file mode 100644 index 00000000..a77d7d92 --- /dev/null +++ b/carl/envs/gymnasium/classic_control/__init__.py @@ -0,0 +1,16 @@ +# flake8: noqa: F401 +from carl.envs.gymnasium.classic_control.carl_acrobot import CARLAcrobot +from carl.envs.gymnasium.classic_control.carl_cartpole import CARLCartPole +from carl.envs.gymnasium.classic_control.carl_mountaincar import CARLMountainCar +from carl.envs.gymnasium.classic_control.carl_mountaincarcontinuous import ( + CARLMountainCarContinuous, +) +from carl.envs.gymnasium.classic_control.carl_pendulum import CARLPendulum + +__all__ = [ + "CARLAcrobot", + "CARLCartPole", + "CARLMountainCar", + "CARLMountainCarContinuous", + "CARLPendulum", +] diff --git a/carl/envs/gymnasium/classic_control/carl_acrobot.py b/carl/envs/gymnasium/classic_control/carl_acrobot.py new file mode 100644 index 00000000..a1c4f167 --- /dev/null +++ b/carl/envs/gymnasium/classic_control/carl_acrobot.py @@ -0,0 +1,66 @@ +from __future__ import annotations + +import numpy as np + +from carl.context.context_space import ContextFeature, UniformFloatContextFeature +from carl.envs.gymnasium.carl_gymnasium_env import CARLGymnasiumEnv + + +class CARLAcrobot(CARLGymnasiumEnv): + env_name: str = "Acrobot-v1" + + @staticmethod + def get_context_features() -> dict[str, ContextFeature]: + return { + "LINK_LENGTH_1": UniformFloatContextFeature( + "LINK_LENGTH_1", lower=0.1, upper=10, default_value=1 + ), + "LINK_LENGTH_2": UniformFloatContextFeature( + "LINK_LENGTH_2", lower=0.1, upper=10, default_value=1 + ), + "LINK_MASS_1": UniformFloatContextFeature( + "LINK_MASS_1", lower=0.1, upper=10, default_value=1 + ), + "LINK_MASS_2": UniformFloatContextFeature( + "LINK_MASS_2", lower=0.1, upper=10, default_value=1 + ), + "LINK_COM_POS_1": UniformFloatContextFeature( + "LINK_COM_POS_1", lower=0, upper=1, default_value=0.5 + ), + "LINK_COM_POS_2": UniformFloatContextFeature( + "LINK_COM_POS_2", lower=0, upper=1, default_value=0.5 + ), + "LINK_MOI": UniformFloatContextFeature( + "LINK_MOI", lower=0.1, upper=10, default_value=1 + ), + "MAX_VEL_1": UniformFloatContextFeature( + "MAX_VEL_1", + lower=0.4 * np.pi, + upper=40 * np.pi, + default_value=4 * np.pi, + ), + "MAX_VEL_2": UniformFloatContextFeature( + "MAX_VEL_2", + lower=0.9 * np.pi, + upper=90 * np.pi, + default_value=9 * np.pi, + ), + "torque_noise_max": UniformFloatContextFeature( + "torque_noise_max", lower=-1, upper=1, default_value=0 + ), + "INITIAL_ANGLE_LOWER": UniformFloatContextFeature( + "INITIAL_ANGLE_LOWER", lower=-np.inf, upper=np.inf, default_value=-0.1 + ), + "INITIAL_ANGLE_UPPER": UniformFloatContextFeature( + "INITIAL_ANGLE_UPPER", lower=-np.inf, upper=np.inf, default_value=0.1 + ), + "INITIAL_VELOCITY_LOWER": UniformFloatContextFeature( + "INITIAL_VELOCITY_LOWER", + lower=-np.inf, + upper=np.inf, + default_value=-0.1, + ), + "INITIAL_VELOCITY_UPPER": UniformFloatContextFeature( + "INITIAL_VELOCITY_UPPER", lower=-np.inf, upper=np.inf, default_value=0.1 + ), + } diff --git a/carl/envs/gymnasium/classic_control/carl_cartpole.py b/carl/envs/gymnasium/classic_control/carl_cartpole.py new file mode 100644 index 00000000..93c663b8 --- /dev/null +++ b/carl/envs/gymnasium/classic_control/carl_cartpole.py @@ -0,0 +1,39 @@ +from __future__ import annotations + +import numpy as np + +from carl.context.context_space import ContextFeature, UniformFloatContextFeature +from carl.envs.gymnasium.carl_gymnasium_env import CARLGymnasiumEnv + + +class CARLCartPole(CARLGymnasiumEnv): + env_name: str = "CartPole-v1" + + @staticmethod + def get_context_features() -> dict[str, ContextFeature]: + return { + "gravity": UniformFloatContextFeature( + "gravity", lower=0.1, upper=np.inf, default_value=9.8 + ), + "masscart": UniformFloatContextFeature( + "masscart", lower=0.1, upper=10, default_value=1.0 + ), + "masspole": UniformFloatContextFeature( + "masspole", lower=0.01, upper=1, default_value=0.1 + ), + "length": UniformFloatContextFeature( + "length", lower=0.05, upper=5, default_value=0.5 + ), + "force_mag": UniformFloatContextFeature( + "force_mag", lower=1, upper=100, default_value=10.0 + ), + "tau": UniformFloatContextFeature( + "tau", lower=0.002, upper=0.2, default_value=0.02 + ), + "initial_state_lower": UniformFloatContextFeature( + "initial_state_lower", lower=-np.inf, upper=np.inf, default_value=-0.1 + ), + "initial_state_upper": UniformFloatContextFeature( + "initial_state_upper", lower=-np.inf, upper=np.inf, default_value=0.1 + ), + } diff --git a/carl/envs/gymnasium/classic_control/carl_mountaincar.py b/carl/envs/gymnasium/classic_control/carl_mountaincar.py new file mode 100644 index 00000000..ac8b49ef --- /dev/null +++ b/carl/envs/gymnasium/classic_control/carl_mountaincar.py @@ -0,0 +1,48 @@ +from __future__ import annotations + +import numpy as np + +from carl.context.context_space import ContextFeature, UniformFloatContextFeature +from carl.envs.gymnasium.carl_gymnasium_env import CARLGymnasiumEnv + + +class CARLMountainCar(CARLGymnasiumEnv): + env_name: str = "MountainCar-v0" + + @staticmethod + def get_context_features() -> dict[str, ContextFeature]: + return { + "min_position": UniformFloatContextFeature( + "min_position", lower=-np.inf, upper=np.inf, default_value=-1.2 + ), + "max_position": UniformFloatContextFeature( + "max_position", lower=-np.inf, upper=np.inf, default_value=0.6 + ), + "max_speed": UniformFloatContextFeature( + "max_speed", lower=0, upper=np.inf, default_value=0.07 + ), + "goal_position": UniformFloatContextFeature( + "goal_position", lower=-np.inf, upper=np.inf, default_value=0.45 + ), + "goal_velocity": UniformFloatContextFeature( + "goal_velocity", lower=-np.inf, upper=np.inf, default_value=0 + ), + "force": UniformFloatContextFeature( + "force", lower=-np.inf, upper=np.inf, default_value=0.001 + ), + "gravity": UniformFloatContextFeature( + "gravity", lower=0, upper=np.inf, default_value=0.0025 + ), + "min_position_start": UniformFloatContextFeature( + "min_position_start", lower=-np.inf, upper=np.inf, default_value=-0.6 + ), + "max_position_start": UniformFloatContextFeature( + "max_position_start", lower=-np.inf, upper=np.inf, default_value=-0.4 + ), + "min_velocity_start": UniformFloatContextFeature( + "min_velocity_start", lower=-np.inf, upper=np.inf, default_value=0 + ), + "max_velocity_start": UniformFloatContextFeature( + "max_velocity_start", lower=-np.inf, upper=np.inf, default_value=0 + ), + } diff --git a/carl/envs/gymnasium/classic_control/carl_mountaincarcontinuous.py b/carl/envs/gymnasium/classic_control/carl_mountaincarcontinuous.py new file mode 100644 index 00000000..b16f357e --- /dev/null +++ b/carl/envs/gymnasium/classic_control/carl_mountaincarcontinuous.py @@ -0,0 +1,45 @@ +from __future__ import annotations + +import numpy as np + +from carl.context.context_space import ContextFeature, UniformFloatContextFeature +from carl.envs.gymnasium.carl_gymnasium_env import CARLGymnasiumEnv + + +class CARLMountainCarContinuous(CARLGymnasiumEnv): + env_name: str = "MountainCarContinuous-v0" + + @staticmethod + def get_context_features() -> dict[str, ContextFeature]: + return { + "min_position": UniformFloatContextFeature( + "min_position", lower=-np.inf, upper=np.inf, default_value=-1.2 + ), + "max_position": UniformFloatContextFeature( + "max_position", lower=-np.inf, upper=np.inf, default_value=0.6 + ), + "max_speed": UniformFloatContextFeature( + "max_speed", lower=0, upper=np.inf, default_value=0.07 + ), + "goal_position": UniformFloatContextFeature( + "goal_position", lower=-np.inf, upper=np.inf, default_value=0.5 + ), + "goal_velocity": UniformFloatContextFeature( + "goal_velocity", lower=-np.inf, upper=np.inf, default_value=0 + ), + "power": UniformFloatContextFeature( + "power", lower=-np.inf, upper=np.inf, default_value=0.0015 + ), + "min_position_start": UniformFloatContextFeature( + "min_position_start", lower=-np.inf, upper=np.inf, default_value=-0.6 + ), + "max_position_start": UniformFloatContextFeature( + "max_position_start", lower=-np.inf, upper=np.inf, default_value=-0.4 + ), + "min_velocity_start": UniformFloatContextFeature( + "min_velocity_start", lower=-np.inf, upper=np.inf, default_value=0 + ), + "max_velocity_start": UniformFloatContextFeature( + "max_velocity_start", lower=-np.inf, upper=np.inf, default_value=0 + ), + } diff --git a/carl/envs/gymnasium/classic_control/carl_pendulum.py b/carl/envs/gymnasium/classic_control/carl_pendulum.py new file mode 100644 index 00000000..29f314a8 --- /dev/null +++ b/carl/envs/gymnasium/classic_control/carl_pendulum.py @@ -0,0 +1,36 @@ +from __future__ import annotations + +import numpy as np + +from carl.context.context_space import ContextFeature, UniformFloatContextFeature +from carl.envs.gymnasium.carl_gymnasium_env import CARLGymnasiumEnv + + +class CARLPendulum(CARLGymnasiumEnv): + env_name: str = "Pendulum-v1" + + @staticmethod + def get_context_features() -> dict[str, ContextFeature]: + return { + "gravity": UniformFloatContextFeature( + "gravity", lower=-np.inf, upper=np.inf, default_value=8.0 + ), + "dt": UniformFloatContextFeature( + "dt", lower=0, upper=np.inf, default_value=0.05 + ), + "g": UniformFloatContextFeature( + "g", lower=0, upper=np.inf, default_value=10 + ), + "m": UniformFloatContextFeature( + "m", lower=1e-6, upper=np.inf, default_value=1 + ), + "l": UniformFloatContextFeature( + "l", lower=1e-6, upper=np.inf, default_value=1 + ), + "initial_angle_max": UniformFloatContextFeature( + "initial_angle_max", lower=0, upper=np.inf, default_value=np.pi + ), + "initial_velocity_max": UniformFloatContextFeature( + "initial_velocity_max", lower=0, upper=np.inf, default_value=1 + ), + } diff --git a/carl/envs/mario/__init__.py b/carl/envs/mario/__init__.py index c59871f8..76f83dc8 100644 --- a/carl/envs/mario/__init__.py +++ b/carl/envs/mario/__init__.py @@ -6,7 +6,4 @@ except Exception as e: warnings.warn(f"Could not load CARLMarioEnv which is probably not installed ({e}).") -from carl.envs.mario.carl_mario_definitions import CONTEXT_BOUNDS as CARLMarioEnv_bounds -from carl.envs.mario.carl_mario_definitions import ( - DEFAULT_CONTEXT as CARLMarioEnv_defaults, -) +__all__ = ["CARLMarioEnv"] diff --git a/carl/envs/mario/carl_mario.py b/carl/envs/mario/carl_mario.py index 1e8e9edf..05211a03 100644 --- a/carl/envs/mario/carl_mario.py +++ b/carl/envs/mario/carl_mario.py @@ -1,54 +1,55 @@ -from typing import Dict, List, Optional, Union +from __future__ import annotations -import gym +from typing import List +import numpy as np + +from carl.context.context_space import ( + CategoricalContextFeature, + ContextFeature, + UniformFloatContextFeature, +) from carl.context.selection import AbstractSelector from carl.envs.carl_env import CARLEnv -from carl.envs.mario.carl_mario_definitions import ( - DEFAULT_CONTEXT, - INITIAL_HEIGHT, - INITIAL_WIDTH, -) from carl.envs.mario.mario_env import MarioEnv from carl.envs.mario.toad_gan import generate_level -from carl.utils.trial_logger import TrialLogger -from carl.utils.types import Context, Contexts +from carl.utils.types import Contexts + +try: + from carl.envs.mario.toad_gan import generate_initial_noise +except FileNotFoundError: + from torch import Tensor + + def generate_initial_noise(width: int, height: int, level_index: int) -> Tensor: + return Tensor() + + +INITIAL_HEIGHT = 16 +INITIAL_WIDTH = 100 class CARLMarioEnv(CARLEnv): def __init__( self, - env: gym.Env = MarioEnv(levels=[]), - contexts: Contexts = {}, - hide_context: bool = True, - add_gaussian_noise_to_context: bool = False, - gaussian_noise_std_percentage: float = 0.05, - logger: Optional[TrialLogger] = None, - scale_context_features: str = "no", - default_context: Optional[Context] = DEFAULT_CONTEXT, - state_context_features: Optional[List[str]] = None, - context_mask: Optional[List[str]] = None, - dict_observation_space: bool = False, - context_selector: Optional[ - Union[AbstractSelector, type[AbstractSelector]] - ] = None, - context_selector_kwargs: Optional[Dict] = None, + env: MarioEnv = None, + contexts: Contexts | None = None, + obs_context_features: list[str] + | None = None, # list the context features which should be added to the state + obs_context_as_dict: bool = True, + context_selector: AbstractSelector | type[AbstractSelector] | None = None, + context_selector_kwargs: dict = None, + **kwargs, ): - if not contexts: - contexts = {0: DEFAULT_CONTEXT} + if env is None: + env = MarioEnv(levels=[]) super().__init__( env=env, contexts=contexts, - hide_context=True, - add_gaussian_noise_to_context=add_gaussian_noise_to_context, - gaussian_noise_std_percentage=gaussian_noise_std_percentage, - logger=logger, - scale_context_features="no", - default_context=default_context, - dict_observation_space=dict_observation_space, + obs_context_features=obs_context_features, + obs_context_as_dict=obs_context_as_dict, context_selector=context_selector, context_selector_kwargs=context_selector_kwargs, - context_mask=context_mask, + **kwargs, ) self.levels: List[str] = [] self._update_context() @@ -75,3 +76,23 @@ def _log_context(self) -> None: self.logger.write_context( self.episode_counter, self.total_timestep_counter, loggable_context ) + + @staticmethod + def get_context_features() -> dict[str, ContextFeature]: + return { + "level_index": CategoricalContextFeature( + "level_index", choices=np.arange(0, 14), default_value=0 + ), + "noise": UniformFloatContextFeature( + "noise", + lower=-1.0, + upper=1.0, + default_value=generate_initial_noise(INITIAL_WIDTH, INITIAL_HEIGHT, 0), + ), + "mario_state": CategoricalContextFeature( + "mario_state", choices=[0, 1, 2], default_value=0 + ), + "mario_inertia": UniformFloatContextFeature( + "mario_inertia", lower=0.5, upper=1.5, default_value=0.89 + ), + } diff --git a/carl/envs/mario/carl_mario_definitions.py b/carl/envs/mario/carl_mario_definitions.py deleted file mode 100644 index 8cdabafe..00000000 --- a/carl/envs/mario/carl_mario_definitions.py +++ /dev/null @@ -1,27 +0,0 @@ -import numpy as np -from torch import Tensor - -try: - from carl.envs.mario.toad_gan import generate_initial_noise -except FileNotFoundError: - - def generate_initial_noise(width: int, height: int, level_index: int) -> Tensor: - return Tensor() - - -INITIAL_WIDTH = 100 -INITIAL_LEVEL_INDEX = 0 -INITIAL_HEIGHT = 16 -DEFAULT_CONTEXT = { - "level_index": INITIAL_LEVEL_INDEX, - "noise": generate_initial_noise(INITIAL_WIDTH, INITIAL_HEIGHT, INITIAL_LEVEL_INDEX), - "mario_state": 0, - "mario_inertia": 0.89, -} -CONTEXT_BOUNDS = { - "level_index": (None, None, "categorical", np.arange(0, 14)), - "noise": (-1.0, 1.0, float), - "mario_state": (None, None, "categorical", [0, 1, 2]), - "mario_inertia": (0.5, 1.5, float), -} -CATEGORICAL_CONTEXT_FEATURES = ["level_index", "mario_state"] diff --git a/carl/envs/mario/generate_sample.py b/carl/envs/mario/generate_sample.py index 39a95222..3b64ca15 100644 --- a/carl/envs/mario/generate_sample.py +++ b/carl/envs/mario/generate_sample.py @@ -29,7 +29,6 @@ def generate_sample( gen_start_scale: int = 0, initial_noise: Optional[Tensor] = None, ) -> List[str]: - in_s = None images_cur: List[Tensor] = [] images: List[Tensor] = [] diff --git a/carl/envs/mario/level_image_gen.py b/carl/envs/mario/level_image_gen.py index 17378462..29c707e3 100644 --- a/carl/envs/mario/level_image_gen.py +++ b/carl/envs/mario/level_image_gen.py @@ -120,7 +120,8 @@ def prepare_sprite_and_box( self, ascii_level: List[str], sprite_key: str, curr_x: int, curr_y: int ) -> Tuple[Any, Tuple[int, int, int, int]]: """Helper to make correct sprites and sprite sizes to draw into the image. - Some sprites are bigger than one tile and the renderer needs to adjust for them.""" + Some sprites are bigger than one tile and the renderer needs to adjust for them. + """ # Init default size new_left = curr_x * 16 @@ -215,7 +216,6 @@ def prepare_sprite_and_box( actual_sprite = self.sprite_dict["b2"] elif sprite_key in ["T", "t"]: # Pipes - # figure out what kind of pipe this is if curr_y > 0 and ascii_level[curr_y - 1][curr_x] == sprite_key: is_top = False diff --git a/carl/envs/mario/mario_env.py b/carl/envs/mario/mario_env.py index 127a75db..2ed0d21d 100644 --- a/carl/envs/mario/mario_env.py +++ b/carl/envs/mario/mario_env.py @@ -6,11 +6,11 @@ from collections import deque import cv2 -import gym +import gymnasium as gym import numpy as np -from gym import spaces -from gym.core import ObsType -from gym.utils import seeding +from gymnasium import spaces +from gymnasium.core import ObsType +from gymnasium.utils import seeding from PIL import Image from py4j.java_gateway import GatewayParameters, JavaGateway @@ -84,7 +84,6 @@ def reset( self, *, seed: Optional[int] = None, - return_info: bool = False, options: Optional[dict] = None, ) -> Union[ObsType, tuple[ObsType, dict]]: self._reset_obs() @@ -97,10 +96,7 @@ def reset( buffer = self._receive() frame = self._read_frame(buffer) self._update_obs(frame) - if not return_info: - return self._obs.copy() - else: - return self._obs.copy(), {} + return self._obs.copy(), {} def step(self, action: Any) -> Any: if self.sticky_action_probability != 0.0: @@ -138,6 +134,7 @@ def step(self, action: Any) -> Any: self._obs.copy(), reward if not self.sparse_rewards else int(completionPercentage == 1.0), done, # bool + False, info, # Dict[str, Any] ) diff --git a/carl/envs/mario/readme.md b/carl/envs/mario/readme.md new file mode 100644 index 00000000..e6d11ebc --- /dev/null +++ b/carl/envs/mario/readme.md @@ -0,0 +1,8 @@ +# **CARL RNA Environment** + +You'll need to follow some additional steps to install the Java backend for this environment. Be aware that Mario at this point will not run on any operation system besides Linux. + +Make sure the submodules TOAD-GUI and Mario-AI-Framework are cloned and up to date. To be able to execute the environment, run: +```bash +javac carl/envs/mario/Mario-AI-Framework/**/*.java +``` diff --git a/carl/envs/rna/__init__.py b/carl/envs/rna/__init__.py index e83f1f49..c1995d98 100644 --- a/carl/envs/rna/__init__.py +++ b/carl/envs/rna/__init__.py @@ -2,9 +2,7 @@ # isort: skip_file try: from carl.envs.rna.carl_rna import CARLRnaDesignEnv - from carl.envs.rna.carl_rna_definitions import ( - DEFAULT_CONTEXT as CARLRnaDesignEnv_defaults, - CONTEXT_BOUNDS as CARLRnaDesignEnv_bounds, - ) except Exception as e: - print(e) + print(f"Could not load CARLRnaDesignEnv which is probably not installed ({e}).") + +__all__ = ["CARLRnaDesignEnv"] diff --git a/carl/envs/rna/carl_rna.py b/carl/envs/rna/carl_rna.py index a04d4f04..a2370f84 100644 --- a/carl/envs/rna/carl_rna.py +++ b/carl/envs/rna/carl_rna.py @@ -1,47 +1,41 @@ # pylint: disable=missing-module-docstring # isort: skip_file -from typing import Optional, Dict, Union, List, Tuple, Any +from __future__ import annotations +from typing import Optional, List, Tuple, Any import numpy as np -import gym - +import gymnasium as gym +from itertools import chain, combinations from carl.envs.carl_env import CARLEnv from carl.envs.rna.parse_dot_brackets import parse_dot_brackets from carl.envs.rna.rna_environment import ( RnaDesignEnvironment, RnaDesignEnvironmentConfig, ) -from carl.utils.trial_logger import TrialLogger -from carl.envs.rna.carl_rna_definitions import ( - DEFAULT_CONTEXT, - ACTION_SPACE, - OBSERVATION_SPACE, - CONTEXT_BOUNDS, +from carl.utils.types import Contexts +from carl.context.context_space import ( + ContextFeature, + UniformFloatContextFeature, + CategoricalContextFeature, ) -from carl.utils.types import Context, Contexts from carl.context.selection import AbstractSelector +ACTION_SPACE = gym.spaces.Discrete(4) +OBSERVATION_SPACE = gym.spaces.Box(low=-np.inf * np.ones(11), high=np.inf * np.ones(11)) + class CARLRnaDesignEnv(CARLEnv): def __init__( self, - env: gym.Env = None, - data_location: str = "carl/envs/rna/learna/data", - contexts: Contexts = {}, - hide_context: bool = False, - add_gaussian_noise_to_context: bool = False, - gaussian_noise_std_percentage: float = 0.01, - logger: Optional[TrialLogger] = None, - scale_context_features: str = "no", - default_context: Optional[Context] = DEFAULT_CONTEXT, - max_episode_length: int = 500, - state_context_features: Optional[List[str]] = None, - context_mask: Optional[List[str]] = None, - dict_observation_space: bool = False, - context_selector: Optional[ - Union[AbstractSelector, type[AbstractSelector]] - ] = None, - context_selector_kwargs: Optional[Dict] = None, + env: RnaDesignEnvironment | None = None, + contexts: Contexts | None = None, + obs_context_features: list[str] + | None = None, # list the context features which should be added to the state + obs_context_as_dict: bool = True, + context_selector: AbstractSelector | type[AbstractSelector] | None = None, + context_selector_kwargs: dict = None, obs_low: Optional[int] = 11, obs_high: Optional[int] = 11, + data_location: str = "carl/envs/rna/learna/data", + **kwargs, ): """ Parameters @@ -52,18 +46,17 @@ def __init__( Different contexts / different environment parameter settings. instance_mode: str, optional """ - if not contexts: - contexts = {0: DEFAULT_CONTEXT} if env is None: + context_space = self.get_context_features() env_config = RnaDesignEnvironmentConfig( - mutation_threshold=DEFAULT_CONTEXT["mutation_threshold"], - reward_exponent=DEFAULT_CONTEXT["reward_exponent"], - state_radius=DEFAULT_CONTEXT["state_radius"], + mutation_threshold=context_space["mutation_threshold"].default_value, + reward_exponent=context_space["reward_exponent"].default_value, + state_radius=context_space["state_radius"].default_value, ) dot_brackets = parse_dot_brackets( - dataset=DEFAULT_CONTEXT["dataset"], # type: ignore[arg-type] + dataset=context_space["dataset"].default_value, # type: ignore[arg-type] # type: ignore[arg-type] data_dir=data_location, - target_structure_ids=DEFAULT_CONTEXT["target_structure_ids"], # type: ignore[arg-type] + target_structure_ids=context_space["target_structure_ids"].default_value, # type: ignore[arg-type] ) env = RnaDesignEnvironment(dot_brackets, env_config) @@ -77,28 +70,21 @@ def __init__( super().__init__( env=env, contexts=contexts, - hide_context=hide_context, - add_gaussian_noise_to_context=add_gaussian_noise_to_context, - gaussian_noise_std_percentage=gaussian_noise_std_percentage, - logger=logger, - scale_context_features=scale_context_features, - default_context=default_context, - max_episode_length=max_episode_length, - state_context_features=state_context_features, - dict_observation_space=dict_observation_space, + obs_context_features=obs_context_features, + obs_context_as_dict=obs_context_as_dict, context_selector=context_selector, context_selector_kwargs=context_selector_kwargs, - context_mask=context_mask, + **kwargs, ) - self.whitelist_gaussian_noise = list(DEFAULT_CONTEXT) + self.whitelist_gaussian_noise = list(self.get_context_features().keys()) self.obs_low = obs_low self.obs_high = obs_high - def step(self, action: np.ndarray) -> Tuple[List[int], float, Any, Any]: + def step(self, action: np.ndarray) -> Tuple[List[int], float, Any, Any, Any]: # Step function has a different name in this env - state, reward, done = self.env.execute(action) # type: ignore[has-type] + state, reward, terminated, truncated = self.env.execute(action) # type: ignore[has-type] self.step_counter += 1 - return state, reward, done, {} + return state, reward, terminated, truncated, {} def _update_context(self) -> None: dot_brackets = parse_dot_brackets( @@ -112,8 +98,37 @@ def _update_context(self) -> None: state_radius=self.context["state_radius"], ) self.env = RnaDesignEnvironment(dot_brackets, env_config) - self.build_observation_space( - env_lower_bounds=-np.inf * np.ones(self.obs_low), - env_upper_bounds=np.inf * np.ones(self.obs_high), - context_bounds=CONTEXT_BOUNDS, # type: ignore[arg-type] - ) + # self.build_observation_space( + # env_lower_bounds=-np.inf * np.ones(self.obs_low), + # env_upper_bounds=np.inf * np.ones(self.obs_high), + # context_bounds=CONTEXT_BOUNDS, # type: ignore[arg-type] + # ) + + @staticmethod + def get_context_features() -> dict[str, ContextFeature]: + # TODO: these actually depend on the dataset, how to handle this? + base_ids = list(range(1, 11)) + id_choices = list( + chain( + *map(lambda x: combinations(base_ids, x), range(0, len(base_ids) + 1)) + ) + ) + [False] + return { + "mutation_threshold": UniformFloatContextFeature( + "mutation_threshold", lower=0.1, upper=np.inf, default_value=5 + ), + "reward_exponent": UniformFloatContextFeature( + "reward_exponent", lower=0.1, upper=np.inf, default_value=1 + ), + "state_radius": UniformFloatContextFeature( + "state_radius", lower=1, upper=np.inf, default_value=5 + ), + "dataset": CategoricalContextFeature( + "dataset", + choices=["eterna", "rfam_learn", "rfam_taneda"], + default_value="eterna", + ), + "target_structure_ids": CategoricalContextFeature( + name="target_structure_ids", choices=id_choices, default_value=False + ), + } diff --git a/carl/envs/rna/carl_rna_definitions.py b/carl/envs/rna/carl_rna_definitions.py index 34051af2..8b5b012f 100644 --- a/carl/envs/rna/carl_rna_definitions.py +++ b/carl/envs/rna/carl_rna_definitions.py @@ -1,5 +1,5 @@ import numpy as np -from gym import spaces +from gymnasium import spaces DEFAULT_CONTEXT = { "mutation_threshold": 5, diff --git a/carl/envs/rna/data/download_and_build_eterna.py b/carl/envs/rna/data/download_and_build_eterna.py index 8669d725..e1fe2dd7 100644 --- a/carl/envs/rna/data/download_and_build_eterna.py +++ b/carl/envs/rna/data/download_and_build_eterna.py @@ -1,6 +1,7 @@ # flake8: noqa: F401 # # isort: skip_file from urllib.request import Request + from tqdm import tqdm import requests # type: ignore[import] @@ -29,7 +30,6 @@ def _download_dataset_from_http(url: str, download_path: str) -> None: def download_eterna(download_path: str) -> None: - eterna_url = ( "https://ars.els-cdn.com/content/image/1-s2.0-S0022283615006567-mmc5.txt" ) @@ -46,6 +46,7 @@ def extract_secondarys(download_path: str, dump_path: str) -> None: dump_path : str path to dump secondary features """ + with open(download_path) as input: parsed = list(zip(*(line.strip().split("\t") for line in input))) diff --git a/carl/envs/rna/data/parse_dot_brackets.py b/carl/envs/rna/data/parse_dot_brackets.py index c08afffe..59c003f0 100644 --- a/carl/envs/rna/data/parse_dot_brackets.py +++ b/carl/envs/rna/data/parse_dot_brackets.py @@ -12,7 +12,6 @@ def parse_dot_brackets( target_structure_ids: List[int] = None, target_structure_path: Path = None, ) -> List[str]: - """Generate the targets for next epoch. The most common encoding for the RNA secondary structure is the dot-bracket diff --git a/carl/envs/rna/rna_environment.py b/carl/envs/rna/rna_environment.py index 073b9dad..14740eac 100644 --- a/carl/envs/rna/rna_environment.py +++ b/carl/envs/rna/rna_environment.py @@ -2,7 +2,7 @@ """ Code adapted from https://github.com/automl/learna """ - +from __future__ import annotations import time from itertools import product @@ -12,9 +12,8 @@ import numpy as np from RNA import fold -import gym - -from typing import Any, List +import gymnasium as gym +from typing import Any @dataclass @@ -97,7 +96,6 @@ def _encode_dot_bracket( # type: ignore[no-untyped-def] def _encode_pairing(secondary: str): # type: ignore[no-untyped-def] - pairing_encoding = [None] * len(secondary) stack = [] for index, symbol in enumerate(secondary, 0): @@ -266,9 +264,7 @@ class RnaDesignEnvironment(gym.Env): The environment for RNA design using deep reinforcement learning. """ - def __init__( # type: ignore[no-untyped-def] - self, dot_brackets: List[str], env_config - ): # type: ignore[no-untyped-def] + def __init__(self, dot_brackets, env_config): """Initialize the environment Args @@ -292,7 +288,9 @@ def __str__(self): # type: ignore[no-untyped-def] def seed(self, seed): # type: ignore[no-untyped-def] return None - def reset(self): # type: ignore[no-untyped-def] + def reset( + self, seed: int | None = None, options: dict[str, Any] | None = None + ) -> tuple[Any, dict[str, Any]]: # type: ignore[no-untyped-def] """ Reset the environment. First function called by runner. Returns first state. Returns: @@ -300,7 +298,7 @@ def reset(self): # type: ignore[no-untyped-def] """ self.target = next(self._target_gen) self.design = _Design(len(self.target)) - return self._get_state() + return self._get_state(), {} def _apply_action(self, action): # type: ignore[no-untyped-def] """ diff --git a/carl/utils/doc_building/plotting.py b/carl/utils/doc_building/plotting.py index 728780b2..a0f92fa7 100644 --- a/carl/utils/doc_building/plotting.py +++ b/carl/utils/doc_building/plotting.py @@ -30,7 +30,6 @@ def radar_factory(num_vars: int, frame: str = "circle") -> np.ndarray: theta = np.linspace(0, 2 * np.pi, num_vars, endpoint=False) class RadarAxes(PolarAxes): - name = "radar" # use 1 line segment to connect specified points RESOLUTION = 1 diff --git a/carl/utils/doc_building/render_brax_env.py b/carl/utils/doc_building/render_brax_env.py index d0adc496..1f40277d 100644 --- a/carl/utils/doc_building/render_brax_env.py +++ b/carl/utils/doc_building/render_brax_env.py @@ -7,7 +7,7 @@ from brax.io import html from IPython.display import HTML - env_name = "fetch" # @param ['ant', 'humanoid', 'fetch', 'grasp', 'halfcheetah', 'ur5e', 'reacher'] + env_name = "ant" # @param ['ant', 'humanoid', 'halfcheetah', ...] env_fn = envs.create_fn(env_name=env_name) env = env_fn() state = env.reset(rng=jax.random.PRNGKey(seed=1)) diff --git a/carl/utils/trial_logger.py b/carl/utils/trial_logger.py deleted file mode 100644 index 877e188d..00000000 --- a/carl/utils/trial_logger.py +++ /dev/null @@ -1,139 +0,0 @@ -from typing import Union - -import argparse -from pathlib import Path - -import configargparse -import pandas as pd - -from carl.utils.types import Context - - -class TrialLogger(object): - """ - Holds all train arguments and sets up logging directory and stables baselines - logging, writes trial setup and writes context feature history. - - Following logging happens at the corresponding events: - after each step: - reward (progress.csv) (StableBaselines logger) - step (progress.csv) (StableBaselines logger) - - after each episode: - context (context_history.csv) (TrialLogger) - episode (context_history.csv) (TrialLogger) - step (context_history.csv) (TrialLogger) - - once on train start: - experiment config (env, agent, seed, set of contexts) (TrialLogger) - hyperparameters - - """ - - def __init__( - self, - logdir: Union[str, Path], - parser: configargparse.ArgParser, - trial_setup_args: argparse.Namespace, - add_context_feature_names_to_logdir: bool = False, - ): - """ - - Parameters - ---------- - logdir: Union[str, Path] - Base logging directory. The actual logging directory, accessible via self.logdir, - is logdir / "{agent}_{seed}". - Agent and seed are provided via trial_setup_args. - If add_context_feature_names_to_logdir is True, - the logging directory will be logdir / context_feature_dirname /f"{agent}_{seed}". - context_feature_dirname are all context feature names provided via - trial_setup_args.context_feature_args joined by "__". - parser: configargparse.ArgParser - Argument parser containing all arguments from runscript. Needed to write - trial setup file. - trial_setup_args: argparse.Namespace - Parsed arguments from parser. Arguments are supposed to be parsed before in case - new arguments are added via some external logic. - add_context_feature_names_to_logdir: bool, False - See logdir for effect. - - """ - self.parser = parser - seed = trial_setup_args.seed - agent = trial_setup_args.agent - if add_context_feature_names_to_logdir: - context_feature_args = trial_setup_args.context_feature_args - names = [ - n for n in context_feature_args if "std" not in n and "mean" not in n - ] # TODO make sure to exclude numbers - context_feature_dirname = "default" - if names: - context_feature_dirname = ( - names[0] if len(names) == 1 else "__".join(names) - ) - self.logdir = Path(logdir) / context_feature_dirname / f"{agent}_{seed}" - else: - self.logdir = Path(logdir) / f"{agent}_{seed}" - self.logdir.mkdir(parents=True, exist_ok=True) - - self.trial_setup_args = trial_setup_args - self.trial_setup_fn = self.logdir / "trial_setup.ini" - - self.context_history_fn = self.logdir / "context_history.csv" - self.prepared_context_history_file = False - - def write_trial_setup(self) -> None: - """ - Write trial setup to file with path logdir / "trial_setup.ini". - - Returns - ------- - None - - """ - output_file_paths = [str(self.trial_setup_fn)] - self.parser.write_config_file( - parsed_namespace=self.trial_setup_args, output_file_paths=output_file_paths - ) - - def write_context(self, episode: int, step: int, context: Context) -> None: - """ - Context will be written to csv file (logdir / "context_history.csv"). - - The format is as follows: - episode,step,context_feature_0,context_feature_1,...,context_feature_n - 0,1,345345,234234,...,234234 - - Parameters - ---------- - episode: int - Episode. - step: int - Timestep. - context: Context - Keys: Context features names/ids, values: context feature values. - - Returns - ------- - None - """ - columns = ["episode", "step"] + list(context.keys()) - values = [episode, step] + list(context.values()) - df = pd.DataFrame(values).T - df.columns = columns - - write_header = False - mode = "a" - if not self.prepared_context_history_file: - write_header = True - mode = "w" - self.prepared_context_history_file = True - - df.to_csv( - path_or_buf=self.context_history_fn, - sep=",", - header=write_header, - index=False, - mode=mode, - ) diff --git a/changelog.md b/changelog.md index d5696ace..76d89dae 100644 --- a/changelog.md +++ b/changelog.md @@ -1,3 +1,18 @@ +# 1.0.0 +Major overhaul of the CARL environment +- Contexts are stored in each environment's class +- Removed deprecate code from CARL env +- CARL env always returns a dict observation, with `obs` and `context` +- Introduction of the context space from which we can conveniently define sampling distributions and sample + +Other +- Update brax environments +- Add docs for dmc environments + +# 0.2.2 +- Make sampling of contexts deterministic with seed +- Update gym to gymnasium + # 0.2.1 - Add Finger (DMC) env - Readd RNA env (#78) diff --git a/docs/Makefile b/docs/Makefile index 52cec997..dfa3be45 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -13,7 +13,12 @@ clean: linkcheck: SPHINX_GALLERY_PLOT=False $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck @echo - @echo "Link check complete; look for any errors in the above output " + @echo "Link check complete; look for any errors in the above output or in $(BUILDDIR)/linkcheck/output.txt." + +linkcheck: + SPHINX_GALLERY_PLOT=False $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck + @echo + @echo "Link check complete; look for any errors in the above output or in $(BUILDDIR)/linkcheck/output.txt." html: $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html @echo @@ -25,7 +30,3 @@ html-noexamples: @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." docs: html linkcheck - -all: - make clean - make docs diff --git a/docs/api.rst b/docs/api.rst new file mode 100644 index 00000000..1fb8c426 --- /dev/null +++ b/docs/api.rst @@ -0,0 +1,6 @@ +.. autosummary:: + :template: module.rst + :toctree: api + :recursive: + + carl.envs diff --git a/docs/conf.py b/docs/conf.py index 66f05178..52b21ed3 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -6,29 +6,31 @@ from carl import copyright, author, version, name -options = {"copyright": copyright, - "author": author, - "version": version, - "versions": { - f"v{version} (stable)": "#", - }, - "name": name, - "html_theme_options": { - "github_url": "https://github.com/automl/automl_sphinx_theme", - "twitter_url": "https://twitter.com/automl_org?lang=de", - }, - #this is here to exclude the examples gallery since they are not documented - "extensions": ["myst_parser", - "sphinx.ext.autodoc", - "sphinx.ext.viewcode", - "sphinx.ext.napoleon", # Enables to understand NumPy docstring - # "numpydoc", - "sphinx.ext.autosummary", - "sphinx.ext.autosectionlabel", - "sphinx_autodoc_typehints", - "sphinx.ext.doctest", - ] - } +options = { + "copyright": copyright, + "author": author, + "version": version, + "versions": { + f"v{version} (stable)": "#", + }, + "name": name, + "html_theme_options": { + "github_url": "https://github.com/automl/automl_sphinx_theme", + "twitter_url": "https://twitter.com/automl_org?lang=de", + }, + # this is here to exclude the examples gallery since they are not documented + "extensions": [ + "myst_parser", + "sphinx.ext.autodoc", + "sphinx.ext.viewcode", + "sphinx.ext.napoleon", # Enables to understand NumPy docstring + # "numpydoc", + "sphinx.ext.autosummary", + "sphinx.ext.autosectionlabel", + "sphinx_autodoc_typehints", + "sphinx.ext.doctest", + ], +} # Import conf.py from the automl theme automl_sphinx_theme.set_options(globals(), options) diff --git a/docs/source/environments/data/screenshots/finger.jpg b/docs/source/environments/data/screenshots/finger.jpg new file mode 100644 index 00000000..b7aa5fc7 Binary files /dev/null and b/docs/source/environments/data/screenshots/finger.jpg differ diff --git a/docs/source/environments/data/screenshots/fish.jpg b/docs/source/environments/data/screenshots/fish.jpg new file mode 100644 index 00000000..7bd88d10 Binary files /dev/null and b/docs/source/environments/data/screenshots/fish.jpg differ diff --git a/docs/source/environments/data/screenshots/quadruped.jpg b/docs/source/environments/data/screenshots/quadruped.jpg new file mode 100644 index 00000000..229c0dc3 Binary files /dev/null and b/docs/source/environments/data/screenshots/quadruped.jpg differ diff --git a/docs/source/environments/data/screenshots/walker.jpg b/docs/source/environments/data/screenshots/walker.jpg new file mode 100644 index 00000000..59ef6264 Binary files /dev/null and b/docs/source/environments/data/screenshots/walker.jpg differ diff --git a/docs/source/environments/data/tab_overview_environments.csv b/docs/source/environments/data/tab_overview_environments.csv index 2dec5e5a..be9f9498 100644 --- a/docs/source/environments/data/tab_overview_environments.csv +++ b/docs/source/environments/data/tab_overview_environments.csv @@ -13,5 +13,9 @@ brax,CARLHumanoid,5,"Box(-1.0, 1.0, (17,), float32)","Box(-inf, inf, (304,), flo brax,CARLFetch,9,"Box(-1.0, 1.0, (10,), float32)","Box(-inf, inf, (110,), float32)" brax,CARLGrasp,9,"Box(-1.0, 1.0, (19,), float32)","Box(-inf, inf, (141,), float32)" brax,CARLUr5e,9,"Box(-1.0, 1.0, (6,), float32)","Box(-inf, inf, (75,), float32)" +dmc,CARLDmcFishEnv,14,"Box(-1.0, 1.0, (5,), float64)","Box(-inf, inf, (24,), float32)" +dmc,CARLDmcFingerEnv,18,"Box(-1.0, 1.0, (2,), float64)","Box(-inf, inf, (9,), float32)" +dmc,CARLDmcQuadrupedEnv,14,"Box(-1.0, 1.1, (12,), float64)","Box(-inf, inf, (78,), float32)" +dmc,CARLDmcWalkerEnv,14,"Box(-1.0, 1.0, (6,), float64)","Box(-inf, inf, (24,), float32)" RNA,CARLRnaDesignEnv,5,Discrete(4),"Box(-inf, inf, (11,), float32)" Mario,CARLMarioEnv,4,Discrete(10),"Box(0, 255, (4, 64, 64), uint8)" diff --git a/docs/source/environments/environment_families/box2d.rst b/docs/source/environments/environment_families/box2d.rst index 5009db29..9000b36d 100644 --- a/docs/source/environments/environment_families/box2d.rst +++ b/docs/source/environments/environment_families/box2d.rst @@ -25,9 +25,14 @@ CARL LunarLander Environment ------------------------------ .. image:: ../data/screenshots/lunarlander.jpeg :width: 25% - :align: center + :align: left :alt: Screenshot of CARLLunarLanderEnv +.. image:: ../data/context_generalization_plots/plot_ecdf_CARLLunarLanderEnv.png + :width: 50% + :align: right + :alt: Influence of context settings on an agent trained on the default environment. + Here, the lunar lander should be safely navigated to its landing pad. The lander's body, physics and simulation dynamics can be manipulated via the context features. diff --git a/docs/source/environments/environment_families/classic_control.rst b/docs/source/environments/environment_families/classic_control.rst index 88a9890a..2150b2a7 100644 --- a/docs/source/environments/environment_families/classic_control.rst +++ b/docs/source/environments/environment_families/classic_control.rst @@ -9,9 +9,14 @@ CARL Pendulum Environment ------------------------- .. image:: ../data/screenshots/pendulum.jpeg :width: 25% - :align: center + :align: left :alt: Pendulum Environment +.. image:: ../data/context_generalization_plots/plot_ecdf_CARLPendulumEnv.png + :width: 50% + :align: right + :alt: Influence of context settings on an agent trained on the default environment. + In Pendulum, the agent's task is to swing up an inverted pendulum and balance it at the top from a random position. The action here is the direction and amount of force the agent wants to apply to the pendulum. @@ -25,9 +30,14 @@ CARL CartPole Environment ------------------------- .. image:: ../data/screenshots/cartpole.jpeg :width: 25% - :align: center + :align: left :alt: CartPole Environment +.. image:: ../data/context_generalization_plots/plot_ecdf_CARLCartPoleEnv.png + :width: 50% + :align: right + :alt: Influence of context settings on an agent trained on the default environment. + CartPole, similarly to Pendulum, asks the agent to balance a pole upright, though this time the agent doesn't directly apply force to the pole but moves a cart on which the pole ist placed either to the left or the right. @@ -41,9 +51,14 @@ CARL Acrobot Environment ------------------------- .. image:: ../data/screenshots/acrobot.jpeg :width: 25% - :align: center + :align: left :alt: Acrobot Environment +.. image:: ../data/context_generalization_plots/plot_ecdf_CARLAcrobotEnv.png + :width: 50% + :align: right + :alt: Influence of context settings on an agent trained on the default environment. + Acrobot is another swing-up task with the goal being swinging the end of the lower of two links up to a given height. The agent accomplishes this by actuating the joint connecting both links. @@ -57,9 +72,14 @@ CARL MountainCar Environment ---------------------------- .. image:: ../data/screenshots/mountaincar.jpeg :width: 25% - :align: center + :align: left :alt: MountainCar Environment +.. image:: ../data/context_generalization_plots/plot_ecdf_CARLMountainCarEnv.png + :width: 50% + :align: right + :alt: Influence of context settings on an agent trained on the default environment. + The MountainCar environment asks the agent to move a car up a steep slope. In order to succeed, the agent has to accelerate using the opposite slope. There are two versions of the environment, a discrete one with only "left" and "right" as actions, diff --git a/docs/source/environments/environment_families/dmc.rst b/docs/source/environments/environment_families/dmc.rst new file mode 100644 index 00000000..d29265de --- /dev/null +++ b/docs/source/environments/environment_families/dmc.rst @@ -0,0 +1,81 @@ +CARL DMC Environments +###################### +CARL includes the Finger, Fish, Quadruped and Walker environments from the `DeepMind Control Suite `_. +The context features control the MuJoCo physics engine, e.g. the floor friction. + + +CARL DMC Finger Environment +*************************** +.. image:: ../data/screenshots/finger.jpg + :width: 25% + :align: center + :alt: Screenshot of CARLDmcFinger + + +The agent needs to learn to spin an object using the finger. + + +.. csv-table:: Defaults and Bounds + :file: ../data/context_definitions/CARLDmcFinger.csv + :header-rows: 1 + + + +CARL DMC Fish Environment +********************** +.. image:: ../data/screenshots/fish.jpg + :width: 25% + :height: 100px + :align: center + :alt: Screenshot of CARLDmcFish + + +In Fish, the agent needs to swim as a simulated fish. + + +.. csv-table:: Defaults and Bounds + :file: ../data/context_definitions/CARLDmcFish.csv + :header-rows: 1 + + + +CARL DMC Quadruped Environment +********************** +.. image:: ../data/screenshots/quadruped.jpg + :width: 25% + :align: center + :alt: Screenshot of CARLDmcQuadruped + +.. image:: ../data/context_generalization_plots/plot_ecdf_CARLDmcQuadrupedEnv.png + :width: 50% + :align: right + :alt: Influence of context settings on an agent trained on the default environment. + + +The agent's goal is to walk efficiently with the quadruped robot. + + +.. csv-table:: Defaults and Bounds + :file: ../data/context_definitions/CARLDmcQuadruped.csv + :header-rows: 1 + + + +CARL DMC Walker Environment +***************************** +.. image:: ../data/screenshots/walker.jpg + :width: 25% + :align: left + :alt: Screenshot of CARLDmcWalker + +.. image:: ../data/context_generalization_plots/plot_ecdf_CARLDmcWalkerEnv.png + :width: 50% + :align: right + :alt: Influence of context settings on an agent trained on the default environment. + +The walker robot is supposed to move forward as fast as possible. + + +.. csv-table:: Defaults and Bounds + :file: ../data/context_definitions/CARLDmcWalker.csv + :header-rows: 1 \ No newline at end of file diff --git a/docs/source/environments/environment_families/index.rst b/docs/source/environments/environment_families/index.rst index 71d5f23b..4d390096 100644 --- a/docs/source/environments/environment_families/index.rst +++ b/docs/source/environments/environment_families/index.rst @@ -46,6 +46,15 @@ like joint strength or torso mass brax +DeepMind Control Suite +---- +Selected environment from the `DeepMind Control Suite `_ with controllable physics. + +.. toctree:: + :maxdepth: 2 + + dmc + Mario ----- `Super Mario (TOAD-GAN) `_, a procedurally generated jump'n'run game with control diff --git a/docs/themes/smac/smac_theme.py b/docs/themes/smac/smac_theme.py index 63b44a95..65dfcbfc 100644 --- a/docs/themes/smac/smac_theme.py +++ b/docs/themes/smac/smac_theme.py @@ -378,7 +378,9 @@ def _get_local_toctree_for( return result -def index_toctree(app, pagename: str, startdepth: int, collapse: bool = False, **kwargs): +def index_toctree( + app, pagename: str, startdepth: int, collapse: bool = False, **kwargs +): """ Returns the "local" (starting at `startdepth`) TOC tree containing the current page, rendered as HTML bullet lists. @@ -440,7 +442,6 @@ def soup_to_python(soup, only_pages=False): # ... def extract_level_recursive(ul, navs_list): - for li in ul.find_all("li", recursive=False): ref = li.a url = ref["href"] @@ -599,6 +600,7 @@ def visit_table(self, node): # ----------------------------------------------------------------------------- + def get_html_theme_path(): """Return list of HTML theme paths.""" theme_path = os.path.abspath(os.path.dirname(__file__)) diff --git a/examples/demo_carracing.py b/examples/demo_carracing.py index 9f76272b..d412b413 100644 --- a/examples/demo_carracing.py +++ b/examples/demo_carracing.py @@ -4,10 +4,13 @@ from typing import Any import numpy as np -import gym +import gymnasium as gym import time import pygame -from carl.envs.box2d.carl_vehicle_racing import CARLVehicleRacingEnv, VEHICLE_NAMES +from carl.envs.gymnasium.box2d.carl_vehicle_racing import ( + CARLVehicleRacing, + VEHICLE_NAMES, +) if __name__ == "__main__": from pyglet.window import key @@ -39,12 +42,12 @@ def register_input(): if event.key == pygame.K_DOWN: a[2] = 0 - contexts = {i: {"VEHICLE": i} for i in range(len(VEHICLE_NAMES))} - env = CARLVehicleRacingEnv(contexts=contexts) - env.render() + contexts = {i: {"VEHICLE_ID": i} for i in range(len(VEHICLE_NAMES))} + env = CARLVehicleRacing(contexts=contexts) + record_video = False if record_video: - from gym.wrappers.record_video import RecordVideo + from gymnasium.wrappers.record_video import RecordVideo env = RecordVideo( env=env, video_folder="/tmp/video-test", name_prefix="CARLVehicleRacing" @@ -53,6 +56,7 @@ def register_input(): isopen = True while isopen: env.reset() + env.render() total_reward = 0.0 steps = 0 restart = False diff --git a/examples/demo_heuristic_lunarlander.py b/examples/demo_heuristic_lunarlander.py index d05e0ed4..688d9bc0 100644 --- a/examples/demo_heuristic_lunarlander.py +++ b/examples/demo_heuristic_lunarlander.py @@ -1,8 +1,8 @@ from typing import Union, Optional -from gym.envs.box2d.lunar_lander import heuristic -import gym.envs.box2d.lunar_lander as lunar_lander - +from gymnasium.envs.box2d.lunar_lander import heuristic +import gymnasium.envs.box2d.lunar_lander as lunar_lander +from gymnasium.utils.step_api_compatibility import step_api_compatibility from carl.envs import CARLLunarLanderEnv @@ -16,22 +16,26 @@ def demo_heuristic_lander( """ Copied from LunarLander """ - env.seed(seed) + total_reward = 0 steps = 0 - env.render() - s = env.reset() + if render: + env.render() + s = env.reset( + seed=seed, + ) + while True: a = heuristic(env, s) - s, r, done, info = env.step(a) + + s, r, done, truncated, info = env.step(a) + total_reward += r - if render: + if render and steps % 20 == 0: still_open = env.render() - if not still_open: - break - if done: # or steps % 20 == 0: + if done or truncated: # or steps % 20 == 0: # print("observations:", " ".join(["{:+0.2f}".format(x) for x in s])) print("step {} total_reward {:+0.2f}".format(steps, total_reward)) steps += 1 diff --git a/examples/manually_switch_contexts_with_dm_control.ipynb b/examples/manually_switch_contexts_with_dm_control.ipynb new file mode 100644 index 00000000..8701b7e6 --- /dev/null +++ b/examples/manually_switch_contexts_with_dm_control.ipynb @@ -0,0 +1,269 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Changing context manually on DMCFinger\n", + "\n", + "We'll take a look at how to manually control contexts in this example on the Deepmind Control Suite." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from carl.context.selection import StaticSelector\n", + "from carl.envs import CARLDmcFingerEnv" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let's define the context. Instead of sampling, we can also manually change values. In this case, we simply take the default context and augment it a bit." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "long_finger = CARLDmcFingerEnv.get_default_context().copy()\n", + "long_finger[\"limb_length_0\"] = long_finger[\"limb_length_0\"] * 2\n", + "long_finger[\"limb_length_1\"] = long_finger[\"limb_length_1\"] * 1.75\n", + "contexts = {0: CARLDmcFingerEnv.get_default_context(), 1: long_finger}" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we can use our contexts to instantiate the CARL environment. We choose a StaticSelector since we don't want the context to change on its own." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Currently using finger limb lengths 0.17 and 0.16\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "env = CARLDmcFingerEnv(context_selector=StaticSelector(contexts))\n", + "render = lambda: plt.imshow(env.render())\n", + "env.reset()\n", + "print(f\"Currently using finger limb lengths {np.round(env.context['limb_length_0'], decimals=2)} and {np.round(env.context['limb_length_1'], decimals=2)}\")\n", + "render()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is how the environment looks with a few random steps." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for _ in range(3):\n", + " action = env.action_space.sample()\n", + " state, reward, terminated, truncated, info = env.step(action=action)\n", + " plt.figure()\n", + " render()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's change to the long finger version instead. Take a look at our new finger:" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Currently using finger limb lengths 0.34 and 0.28\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "env.context_id = 1\n", + "print(f\"Currently using finger limb lengths {np.round(env.context['limb_length_0'], decimals=2)} and {np.round(env.context['limb_length_1'], decimals=2)}\")\n", + "env.reset()\n", + "render()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And now let's take a few steps to see it in action:" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for _ in range(3):\n", + " action = env.action_space.sample()\n", + " state, reward, terminated, truncated, info = env.step(action=action)\n", + " plt.figure()\n", + " render()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "carl", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/play_car_racing.py b/examples/play_car_racing.py new file mode 100644 index 00000000..d36b868a --- /dev/null +++ b/examples/play_car_racing.py @@ -0,0 +1,77 @@ +""" +Code adapted from gym.envs.box2d.car_racing.py + +Play Car Racing with the new CARL vehicles and test out our contexts yourself! +""" + +import numpy as np +import time +import pygame +from carl.envs.gymnasium.box2d.carl_vehicle_racing import ( + CARLVehicleRacing, + VEHICLE_NAMES, +) + +if __name__ == "__main__": + a = np.array([0.0, 0.0, 0.0]) + + def register_input(): + for event in pygame.event.get(): + if event.type == pygame.KEYDOWN: + if event.key == pygame.K_LEFT: + a[0] = -1.0 + if event.key == pygame.K_RIGHT: + a[0] = +1.0 + if event.key == pygame.K_UP: + a[1] = +1.0 + if event.key == pygame.K_DOWN: + a[2] = +0.8 # set 1.0 for wheels to block to zero rotation + if event.key == pygame.K_RETURN: + global restart + restart = True + if event.key == pygame.K_ESCAPE: + global isopen + isopen = False + + if event.type == pygame.KEYUP: + if event.key == pygame.K_LEFT: + a[0] = 0 + if event.key == pygame.K_RIGHT: + a[0] = 0 + if event.key == pygame.K_UP: + a[1] = 0 + if event.key == pygame.K_DOWN: + a[2] = 0 + + contexts = {i: {"VEHICLE_ID": i} for i in range(len(VEHICLE_NAMES))} + env = CARLVehicleRacing(contexts=contexts) + + record_video = False + if record_video: + from gymnasium.wrappers.record_video import RecordVideo + + env = RecordVideo( + env=env, video_folder="/tmp/video-test", name_prefix="CARLVehicleRacing" + ) + + isopen = True + while isopen: + env.reset() + env.render() + total_reward = 0.0 + steps = 0 + restart = False + while True: + register_input() + s, r, terminated, truncated, info = env.step(a) + done = terminated | truncated + time.sleep(0.025) + total_reward += r + if steps % 200 == 0 or done: + print("\naction " + str(["{:+0.2f}".format(x) for x in a])) + print("step {} total_reward {:+0.2f}".format(steps, total_reward)) + steps += 1 + env.render() + if done or restart or not isopen: + break + env.close() diff --git a/examples/sample_contexts_with_brax.ipynb b/examples/sample_contexts_with_brax.ipynb new file mode 100644 index 00000000..78197e3a --- /dev/null +++ b/examples/sample_contexts_with_brax.ipynb @@ -0,0 +1,256 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Context Sampling In CARL\n", + "\n", + "Let's take a look at how we can sample contexts and use them in the environments. We'll use CARLBraxAnt for a little demonstration." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "from carl.context.context_space import NormalFloatContextFeature\n", + "from carl.context.sampler import ContextSampler\n", + "from carl.envs import CARLBraxAnt" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each environment has an associated context space. Before even instantiating the environment, it let's you take a look at which features can be used and what their default values and bounds are." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Context feature names for Ant: ['gravity', 'friction', 'elasticity', 'ang_damping', 'mass_torso', 'viscosity']\n", + "Default context for Ant: {'gravity': -9.8, 'friction': 1.0, 'elasticity': 0.0, 'ang_damping': -0.05, 'mass_torso': 10.0, 'viscosity': 0.0}\n", + "Context value bounds for friction in Ant: (0.0, 100.0)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/theeimer/Documents/git/CARL/carl/envs/brax/carl_ant.py:25: RuntimeWarning: invalid value encountered in scalar divide\n", + " \"ang_damping\": UniformFloatContextFeature(\n" + ] + } + ], + "source": [ + "print(f\"Context feature names for Ant: {CARLBraxAnt.get_context_space().context_feature_names}\")\n", + "print(f\"Default context for Ant: {CARLBraxAnt.get_context_space().get_default_context()}\")\n", + "print(f\"Context value bounds for friction in Ant: {CARLBraxAnt.get_context_space().get_lower_and_upper_bound('friction')}\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use the built-in context sampler to get context values for training. Here we decide we want a normal distribution of float values for the 'gravity' context feature. The context space makes sure we stay within the bounds for the environment. Let's start with 5 contexts for now." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{0: {'gravity': 11.564052345967665}, 1: {'gravity': 10.200157208367225}, 2: {'gravity': 10.77873798410574}, 3: {'gravity': 12.04089319920146}, 4: {'gravity': 11.667557990149968}}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/theeimer/Documents/git/CARL/carl/context/sampler.py:44: DeprecationWarning: Prefer using `list(space.values())` over `get_hyperparameters`\n", + " return self.get_hyperparameters()\n", + "/Users/theeimer/Documents/git/CARL/carl/context/sampler.py:58: DeprecationWarning: `Configuration` act's like a dictionary. Please use `dict(config)` instead of `get_dictionary` if you explicitly need a `dict`\n", + " contexts = [C.get_dictionary() for C in contexts]\n" + ] + } + ], + "source": [ + "seed = 0\n", + "context_distributions = [NormalFloatContextFeature(\"gravity\", mu=9.8, sigma=1)]\n", + "context_sampler = ContextSampler(\n", + " context_distributions=context_distributions,\n", + " context_space=CARLBraxAnt.get_context_space(),\n", + " seed=seed,\n", + " )\n", + "contexts = context_sampler.sample_contexts(n_contexts=5)\n", + "print(contexts)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To use the contexts during training, we simply pass them to the environment:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Full context set: {0: {'gravity': 11.564052345967665, 'friction': 1.0, 'elasticity': 0.0, 'ang_damping': -0.05, 'mass_torso': 10.0, 'viscosity': 0.0}, 1: {'gravity': 10.200157208367225, 'friction': 1.0, 'elasticity': 0.0, 'ang_damping': -0.05, 'mass_torso': 10.0, 'viscosity': 0.0}, 2: {'gravity': 10.77873798410574, 'friction': 1.0, 'elasticity': 0.0, 'ang_damping': -0.05, 'mass_torso': 10.0, 'viscosity': 0.0}, 3: {'gravity': 12.04089319920146, 'friction': 1.0, 'elasticity': 0.0, 'ang_damping': -0.05, 'mass_torso': 10.0, 'viscosity': 0.0}, 4: {'gravity': 11.667557990149968, 'friction': 1.0, 'elasticity': 0.0, 'ang_damping': -0.05, 'mass_torso': 10.0, 'viscosity': 0.0}}\n", + "Current context ID: 0\n", + "Current context: {'gravity': 11.564052345967665}\n" + ] + } + ], + "source": [ + "env = CARLBraxAnt(contexts=contexts)\n", + "print(f\"Full context set: {env.contexts}\")\n", + "env.reset()\n", + "print(f\"Current context ID: {env.context_id}\")\n", + "print(f\"Current context: {env.context}\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we don't specify a context selector, a reset will automatically switch the context to the next one in our context set." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current context ID: 1\n", + "Current context: {'gravity': 10.200157208367225}\n" + ] + } + ], + "source": [ + "env.reset()\n", + "print(f\"Current context ID: {env.context_id}\")\n", + "print(f\"Current context: {env.context}\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also manually set the context by using its ID:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current context ID: 4\n", + "Current context: {'gravity': 11.667557990149968}\n" + ] + } + ], + "source": [ + "env.context_id = 4\n", + "print(f\"Current context ID: {env.context_id}\")\n", + "print(f\"Current context: {env.context}\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Apart from the context, CARLBraxAnt functions like any other gymnasium environment - so your training loops don't have to change at all." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "action = env.action_space.sample()\n", + "state, reward, terminated, truncated, info = env.step(action)\n", + "done = terminated or truncated\n", + "plt.imshow(env.render())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "carl", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/try_dm_control.py b/examples/try_dm_control.py deleted file mode 100644 index 48ff1825..00000000 --- a/examples/try_dm_control.py +++ /dev/null @@ -1,55 +0,0 @@ -# flake8: noqa: F401 -# type: ignore -import matplotlib.pyplot as plt - -from carl.envs import CARLDmcFishEnv -from carl.envs import CARLDmcFishEnv_defaults as fish_default -from carl.envs import CARLDmcFishEnv_mask as fish_mask -from carl.envs import CARLDmcQuadrupedEnv -from carl.envs import CARLDmcQuadrupedEnv_defaults as quadruped_default -from carl.envs import CARLDmcQuadrupedEnv_mask as quadruped_mask -from carl.envs import CARLDmcWalkerEnv -from carl.envs import CARLDmcWalkerEnv_defaults as walker_default -from carl.envs import CARLDmcWalkerEnv_mask as walker_mask - -if __name__ == "__main__": - # Load one task: - stronger_act = walker_default.copy() - stronger_act["actuator_strength"] = walker_default["actuator_strength"] * 2 - contexts = {0: stronger_act} - - # stronger_act = quadruped_default.copy() - # stronger_act["actuator_strength"] = quadruped_default["actuator_strength"]*2 - # contexts = {0: stronger_act} - # carl_env = CARLDmcQuadrupedEnv(task="fetch_context", contexts=contexts, context_mask=quadruped_mask, hide_context=False) - - # contexts = {0: fish_default} - # carl_env = CARLDmcFishEnv(task="upright_context", contexts=contexts, context_mask=fish_mask, hide_context=False) - carl_env = CARLDmcWalkerEnv( - task="run_context", - contexts=contexts, - context_mask=walker_mask, - hide_context=False, - dict_observation_space=True, - ) - action = carl_env.action_space.sample() - state, reward, done, info = carl_env.step(action=action) - print("state", state, type(state)) - - render = lambda: plt.imshow(carl_env.render(mode="rgb_array")) - s = carl_env.reset() - render() - # plt.savefig("dm_render.png") - action = carl_env.action_space.sample() - state, reward, done, info = carl_env.step(action=action) - print("state", state, type(state)) - - # s = carl_env.reset() - # done = False - # i = 0 - # while not done: - # action = carl_env.action_space.sample() - # state, reward, done, info = carl_env.step(action=action) - # print(state, action, reward, done) - # i += 1 - # print(i) diff --git a/examples/try_the_lunarlander_solution_heuristic.ipynb b/examples/try_the_lunarlander_solution_heuristic.ipynb new file mode 100644 index 00000000..c62753c8 --- /dev/null +++ b/examples/try_the_lunarlander_solution_heuristic.ipynb @@ -0,0 +1,284 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Let's demo LunarLander!\n", + "\n", + "We'll use the solution heuristic for lunar lander as well as a random policy to see how the gravity influences the environment.\n", + "First, let's import what we need:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/bigwork/nhwpeimt/CARL/carl/envs/__init__.py:28: UserWarning: Module dm_control not found. If you want to use these environments, please follow the installation guide.\n", + " warnings.warn(\n", + "/bigwork/nhwpeimt/CARL/carl/envs/__init__.py:28: UserWarning: Module distance not found. If you want to use these environments, please follow the installation guide.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "from typing import Union, Optional\n", + "import numpy as np\n", + "\n", + "from gymnasium.envs.box2d.lunar_lander import heuristic\n", + "import gymnasium.envs.box2d.lunar_lander as lunar_lander\n", + "from carl.envs.gymnasium.box2d.carl_lunarlander import CARLLunarLander\n", + "from carl.context.context_space import UniformFloatContextFeature\n", + "from carl.context.sampler import ContextSampler" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we define our training loop using the heuristic to select the actions:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def run_lunar_lander(\n", + " env: Union[\n", + " CARLLunarLander, lunar_lander.LunarLander, lunar_lander.LunarLanderContinuous\n", + " ],\n", + " seed: Optional[int] = None,\n", + " render: bool = True,\n", + ") -> float:\n", + " \"\"\"\n", + " Copied from LunarLander\n", + " \"\"\"\n", + " total_reward = 0\n", + " steps = 0\n", + "\n", + " s, _ = env.reset(\n", + " seed=seed,\n", + " )\n", + " s = s[\"state\"]\n", + "\n", + " if render:\n", + " env.render()\n", + "\n", + " while True:\n", + " a = heuristic(env, s)\n", + "\n", + " s, r, done, truncated, info = env.step(a)\n", + " s = s[\"state\"]\n", + "\n", + " total_reward += r\n", + "\n", + " if render and steps % 20 == 0:\n", + " still_open = env.render()\n", + "\n", + " if done or truncated: # or steps % 20 == 0:\n", + " # print(\"observations:\", \" \".join([\"{:+0.2f}\".format(x) for x in s]))\n", + " print(\"Total reward with heuristic: {:+0.2f}\".format(total_reward))\n", + " steps += 1\n", + " if done:\n", + " break\n", + " return total_reward" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here's the random training loop:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def run_lunar_lander_random(\n", + " env: Union[\n", + " CARLLunarLander, lunar_lander.LunarLander, lunar_lander.LunarLanderContinuous\n", + " ],\n", + " seed: Optional[int] = None,\n", + " render: bool = True,\n", + ") -> float:\n", + " \"\"\"\n", + " Copied from LunarLander\n", + " \"\"\"\n", + " total_reward = 0\n", + " steps = 0\n", + "\n", + " s, _ = env.reset(\n", + " seed=seed,\n", + " )\n", + " s = s[\"state\"]\n", + "\n", + " if render:\n", + " env.render()\n", + "\n", + " while True:\n", + " a = env.action_space.sample()\n", + "\n", + " s, r, done, truncated, info = env.step(a)\n", + " s = s[\"state\"]\n", + "\n", + " total_reward += r\n", + "\n", + " if render and steps % 20 == 0:\n", + " still_open = env.render()\n", + "\n", + " if done or truncated: # or steps % 20 == 0:\n", + " # print(\"observations:\", \" \".join([\"{:+0.2f}\".format(x) for x in s]))\n", + " print(\"Total reward with random policy: {:+0.2f}\".format(total_reward))\n", + " print(\"\\n\")\n", + " steps += 1\n", + " if done:\n", + " break\n", + " return total_reward" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we're ready to go! We'll run 5 times with different gravities:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running LunarLander with gravity 6.63.\n", + "Total reward with heuristic: +297.35\n", + "Total reward with random policy: -83.35\n", + "\n", + "\n", + "Running LunarLander with gravity 5.06.\n", + "Total reward with heuristic: +260.94\n", + "Total reward with random policy: -292.36\n", + "\n", + "\n", + "Running LunarLander with gravity 5.29.\n", + "Total reward with heuristic: +254.62\n", + "Total reward with random policy: -90.59\n", + "\n", + "\n", + "Running LunarLander with gravity 6.65.\n", + "Total reward with heuristic: +244.49\n", + "Total reward with random policy: -107.54\n", + "\n", + "\n", + "Running LunarLander with gravity 11.61.\n", + "Total reward with heuristic: +265.87\n", + "Total reward with random policy: -408.40\n", + "\n", + "\n" + ] + } + ], + "source": [ + "gravities = []\n", + "performance_heuristic = []\n", + "performance_random = []\n", + "for seed in range(5):\n", + " context_distributions = [UniformFloatContextFeature(\"gravity\", upper=12, lower=0.1)]\n", + " context_sampler = ContextSampler(\n", + " context_distributions=context_distributions,\n", + " context_space=CARLLunarLander.get_context_space(),\n", + " seed=seed,\n", + " )\n", + " contexts = context_sampler.sample_contexts(n_contexts=1)\n", + " gravities.append(np.round(contexts[0]['gravity'], decimals=2))\n", + " print(f\"Running LunarLander with gravity {np.round(contexts[0]['gravity'], decimals=2)}.\")\n", + " env = CARLLunarLander()\n", + " performance_heuristic.append(run_lunar_lander(env, seed=seed, render=True))\n", + " performance_random.append(run_lunar_lander_random(env, seed=seed, render=True))\n", + "env.close()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's see the result:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Gravity')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "sns.pointplot(x=gravities, y=performance_heuristic, label=\"heuristic\", color=\"gold\")\n", + "ax = sns.pointplot(x=gravities, y=performance_random, label=\"random\", color='aquamarine')\n", + "ax.legend()\n", + "ax.set_title(\"Landing on different Gravities/Planets\")\n", + "ax.set_ylabel(\"Total episode reward\")\n", + "ax.set_xlabel(\"Gravity\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "carl", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/vary_initial_state_distributions.ipynb b/examples/vary_initial_state_distributions_with_classic_control.ipynb similarity index 100% rename from examples/vary_initial_state_distributions.ipynb rename to examples/vary_initial_state_distributions_with_classic_control.ipynb diff --git a/setup.py b/setup.py index ae5cd938..38e4cf7b 100644 --- a/setup.py +++ b/setup.py @@ -22,7 +22,7 @@ def read_file(filepath: str) -> str: extras_require = { "box2d": [ - "gym[box2d]==0.24.1", + "gymnasium[box2d]>=0.27.1", ], "brax": [ "brax>=0.0.10,<=0.0.16", @@ -32,6 +32,7 @@ def read_file(filepath: str) -> str: "dm_control>=1.0.3", ], "mario": [ + "opencv-python", "torch>=1.9.0", "Pillow>=8.3.1", "py4j>=0.10.9.2", @@ -51,7 +52,8 @@ def read_file(filepath: str) -> str: "sphinx-gallery>=0.10.0", "image>=1.5.33", "sphinx-autoapi>=1.8.4", - ] + "automl-sphinx-theme>=0.1.9", + ], } setuptools.setup( @@ -65,18 +67,15 @@ def read_file(filepath: str) -> str: license_file="LICENSE", url=url, project_urls=project_urls, - keywords=[ - "RL", - "Generalization", - "Context", - "Reinforcement Learning" - ], + keywords=["RL", "Generalization", "Context", "Reinforcement Learning"], version=version, packages=setuptools.find_packages(exclude=["tests"]), include_package_data=True, python_requires=">=3.9", install_requires=[ - "gym==0.24.1", + "gym", + "gymnasium>=0.27.1", + "pygame", "scipy>=1.7.0", "ConfigArgParse>=1.5.1", "numpy>=1.19.5", @@ -90,24 +89,23 @@ def read_file(filepath: str) -> str: "PyYAML>=5.4.1", "tabulate>=0.8.9", "bs4>=0.0.1", + "configspace>=0.7.1", + "omegaconf>=2.3.0", ], extras_require=extras_require, test_suite="pytest", platforms=["Linux"], - entry_points={ - "console_scripts": ["smac = smac.smac_cli:cmd_line_call"], - }, classifiers=[ - "Programming Language :: Python :: 3", - "Natural Language :: English", - "Environment :: Console", - "Intended Audience :: Developers", - "Intended Audience :: Education", - "Intended Audience :: Science/Research", - "License :: OSI Approved :: Apache Software License", - "Operating System :: POSIX :: Linux", - "Topic :: Scientific/Engineering :: Artificial Intelligence", - "Topic :: Scientific/Engineering", - "Topic :: Software Development", + "Programming Language :: Python :: 3", + "Natural Language :: English", + "Environment :: Console", + "Intended Audience :: Developers", + "Intended Audience :: Education", + "Intended Audience :: Science/Research", + "License :: OSI Approved :: Apache Software License", + "Operating System :: POSIX :: Linux", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "Topic :: Scientific/Engineering", + "Topic :: Software Development", ], ) diff --git a/test/test_CARLEnv.py b/test/test_CARLEnv.py index b53ebee0..1c9c1dde 100644 --- a/test/test_CARLEnv.py +++ b/test/test_CARLEnv.py @@ -1,425 +1,31 @@ -from typing import Any, Dict - import unittest -import numpy as np +from carl.envs.gymnasium.classic_control.carl_pendulum import CARLPendulum -from carl.envs.classic_control.carl_pendulum import CARLPendulumEnv -from carl.utils.types import Context +CARLPendulum.render_mode = "rgb_array" class TestStateConstruction(unittest.TestCase): - def test_hiddenstate(self): - """ - Test if we set hide_context = True that we get the original, normal state. - """ - env = CARLPendulumEnv( - contexts={}, - hide_context=True, - add_gaussian_noise_to_context=False, - gaussian_noise_std_percentage=0.01, - state_context_features=None, - ) - env.reset() - action = [0.01] # torque - state, reward, done, info = env.step(action=action) - env.close() - self.assertEqual(3, len(state)) - - def test_visiblestate(self): - """ - Test if we set hide_context = False and state_context_features=None that we get the normal state extended by - all context features. - """ - env = CARLPendulumEnv( - contexts={}, - hide_context=False, - add_gaussian_noise_to_context=False, - gaussian_noise_std_percentage=0.01, - state_context_features=None, - ) - env.reset() - action = [0.01] # torque - state, reward, done, info = env.step(action=action) - env.close() - self.assertEqual(10, len(state)) - - def test_visiblestate_customnone(self): - """ - Test if we set hide_context = False and state_context_features="changing_context_features" that we get the - normal state, not extended by context features. - """ - env = CARLPendulumEnv( - contexts={}, - hide_context=False, - add_gaussian_noise_to_context=False, - gaussian_noise_std_percentage=0.01, - state_context_features=["changing_context_features"], - ) - env.reset() - action = [0.01] # torque - state, reward, done, info = env.step(action=action) - env.close() - # Because we don't change any context features the state length should be 3 - self.assertEqual(3, len(state)) - - def test_visiblestate_custom(self): - """ - Test if we set hide_context = False and state_context_features=["g", "m"] that we get the - normal state, extended by the context feature values of g and m. - """ - env = CARLPendulumEnv( - contexts={}, - hide_context=False, - add_gaussian_noise_to_context=False, - gaussian_noise_std_percentage=0.01, - state_context_features=["g", "m"], - ) - env.reset() - action = [0.01] # torque - state, reward, done, info = env.step(action=action) - env.close() - # state should be of length 5 because we add two context features - self.assertEqual(5, len(state)) - - def test_visiblestate_changingcontextfeatures_nochange(self): - """ - Test if we set hide_context = False and state_context_features="changing_context_features" that we get the - normal state, extended by the context features which are changing in the set of contexts. Here: None are - changing. - """ - contexts = { - "0": {"max_speed": 8.0, "dt": 0.05, "g": 10.0, "m": 1.0, "l": 1.0}, - "1": {"max_speed": 8.0, "dt": 0.05, "g": 10.0, "m": 1.0, "l": 1.0}, - "2": {"max_speed": 8.0, "dt": 0.05, "g": 10.0, "m": 1.0, "l": 1.0}, - "3": {"max_speed": 8.0, "dt": 0.05, "g": 10.0, "m": 1.0, "l": 1.0}, - } - env = CARLPendulumEnv( - contexts=contexts, - hide_context=False, - add_gaussian_noise_to_context=False, - gaussian_noise_std_percentage=0.01, - state_context_features=["changing_context_features"], - ) - env.reset() - action = [0.01] # torque - state, reward, done, info = env.step(action=action) - env.close() - # state should be of length 3 because all contexts are the same - self.assertEqual(3, len(state)) - - def test_visiblestate_changingcontextfeatures_change(self): - """ - Test if we set hide_context = False and state_context_features="changing_context_features" that we get the - normal state, extended by the context features which are changing in the set of contexts. - Here: Two are changing. - """ - contexts = { - "0": {"max_speed": 8.0, "dt": 0.03, "g": 10.0, "m": 1.0, "l": 1.0}, - "1": {"max_speed": 8.0, "dt": 0.05, "g": 10.0, "m": 1.0, "l": 0.95}, - "2": {"max_speed": 8.0, "dt": 0.05, "g": 10.0, "m": 1.0, "l": 0.3}, - "3": {"max_speed": 8.0, "dt": 0.05, "g": 10.0, "m": 1.0, "l": 1.3}, - } - env = CARLPendulumEnv( - contexts=contexts, - hide_context=False, - add_gaussian_noise_to_context=False, - gaussian_noise_std_percentage=0.01, - state_context_features=["changing_context_features"], - ) - env.reset() - action = [0.01] # torque - state, reward, done, info = env.step(action=action) - env.close() - # state should be of length 5 because two features are changing (dt and l) - self.assertEqual(5, len(state)) - - def test_dict_observation_space(self): - contexts = {"0": {"max_speed": 8.0, "dt": 0.03, "g": 10.0, "m": 1.0, "l": 1.0}} - env = CARLPendulumEnv( - contexts=contexts, - hide_context=False, - dict_observation_space=True, - add_gaussian_noise_to_context=False, - gaussian_noise_std_percentage=0.01, - state_context_features=["changing_context_features"], - ) - obs = env.reset() + def test_observation(self): + env = CARLPendulum() + context = CARLPendulum.get_default_context() + obs, info = env.reset() self.assertEqual(type(obs), dict) - self.assertTrue("state" in obs) + self.assertTrue("obs" in obs, msg=str(obs)) self.assertTrue("context" in obs) - action = [0.01] # torque - next_obs, reward, done, info = env.step(action=action) - env.close() - - def test_state_context_feature_population(self): - env = ( # noqa: F841 local variable is assigned to but never used - CARLPendulumEnv( - contexts={}, - hide_context=False, - add_gaussian_noise_to_context=False, - gaussian_noise_std_percentage=0.01, - state_context_features=None, - scale_context_features="no", - ) - ) - self.assertIsNotNone(env.state_context_features) - - -class TestEpisodeTermination(unittest.TestCase): - def test_episode_termination(self): - """ - Test if we set hide_context = True that we get the original, normal state. - """ - ep_length = 100 - env = CARLPendulumEnv( - contexts={}, - hide_context=True, - add_gaussian_noise_to_context=False, - gaussian_noise_std_percentage=0.01, - state_context_features=None, - max_episode_length=ep_length, - ) - env.reset() - action = [0.0] # torque - done = False - counter = 0 - while not done: - state, reward, done, info = env.step(action=action) - counter += 1 - self.assertTrue(counter <= ep_length) - if counter > ep_length: - break - env.close() - - -class TestContextFeatureScaling(unittest.TestCase): - def test_context_feature_scaling_no(self): - env = ( # noqa: F841 local variable is assigned to but never used - CARLPendulumEnv( - contexts={}, - hide_context=False, - add_gaussian_noise_to_context=False, - gaussian_noise_std_percentage=0.01, - state_context_features=None, - scale_context_features="no", - ) - ) - - def test_context_feature_scaling_by_mean(self): - contexts = { - # order is important because context "0" is checked in the test - # because of the reset context "0" must come seond - "1": {"max_speed": 16.0, "dt": 0.06, "g": 20.0, "m": 2.0, "l": 3.6}, - "0": {"max_speed": 8.0, "dt": 0.03, "g": 10.0, "m": 1.0, "l": 1.8}, - } - env = CARLPendulumEnv( - contexts=contexts, - hide_context=False, - add_gaussian_noise_to_context=False, - gaussian_noise_std_percentage=0.01, - state_context_features=None, - scale_context_features="by_mean", - ) - env.reset() - action = [0.0] - state, reward, done, info = env.step(action=action) - n_c = len(env.default_context) - scaled_contexts = state[-n_c:] - target = np.array( - [16 / 12, 0.06 / 0.045, 20 / 15, 2 / 1.5, 3.6 / 2.7, 1, 1] - ) # for context "1" - self.assertTrue( - np.all(target == scaled_contexts), - f"target {target} != actual {scaled_contexts}", - ) - - def test_context_feature_scaling_by_default(self): - default_context = { - "max_speed": 8.0, - "dt": 0.05, - "g": 10.0, - "m": 1.0, - "l": 1.0, - "initial_angle_max": np.pi, - "initial_velocity_max": 1, - } - contexts = { - "0": {"max_speed": 8.0, "dt": 0.03, "g": 10.0, "m": 1.0, "l": 1.8}, - } - env = CARLPendulumEnv( - contexts=contexts, - hide_context=False, - add_gaussian_noise_to_context=False, - gaussian_noise_std_percentage=0.01, - state_context_features=None, - scale_context_features="by_default", - default_context=default_context, - ) - env.reset() - action = [0.0] - state, reward, done, info = env.step(action=action) - n_c = len(default_context) - scaled_contexts = state[-n_c:] - self.assertTrue( - np.all(np.array([1.0, 0.6, 1, 1, 1.8, 1, 1]) == scaled_contexts) - ) - - def test_context_feature_scaling_by_default_nodefcontext(self): - with self.assertRaises(ValueError): - env = CARLPendulumEnv( # noqa: F841 local variable is assigned to but never used - contexts={}, - hide_context=False, - add_gaussian_noise_to_context=False, - gaussian_noise_std_percentage=0.01, - state_context_features=None, - scale_context_features="by_default", - default_context=None, - ) - - def test_context_feature_scaling_unknown_init(self): - with self.assertRaises(ValueError): - env = CARLPendulumEnv( # noqa: F841 local variable is assigned to but never used - contexts={}, - hide_context=False, - add_gaussian_noise_to_context=False, - gaussian_noise_std_percentage=0.01, - state_context_features=None, - scale_context_features="bork", - ) - - def test_context_feature_scaling_unknown_step(self): - env = ( # noqa: F841 local variable is assigned to but never used - CARLPendulumEnv( - contexts={}, - hide_context=False, - add_gaussian_noise_to_context=False, - gaussian_noise_std_percentage=0.01, - state_context_features=None, - scale_context_features="no", - ) - ) - - env.reset() - env.scale_context_features = "bork" - action = [0.01] # torque - with self.assertRaises(ValueError): - next_obs, reward, done, info = env.step(action=action) - - def test_context_mask(self): - context_mask = ["dt", "g"] - env = ( # noqa: F841 local variable is assigned to but never used - CARLPendulumEnv( - contexts={}, - hide_context=False, - context_mask=context_mask, - dict_observation_space=True, - add_gaussian_noise_to_context=False, - gaussian_noise_std_percentage=0.01, - state_context_features=None, - scale_context_features="no", - ) - ) - s = env.reset() - s_c = s["context"] - forbidden_in_context = [ - f for f in env.state_context_features if f in context_mask - ] - self.assertTrue(len(s_c) == len(list(env.default_context.keys())) - 2) - self.assertTrue(len(forbidden_in_context) == 0) - - -class TestContextSelection(unittest.TestCase): - @staticmethod - def generate_contexts() -> Dict[Any, Context]: - keys = "abc" - context = {"max_speed": 8.0, "dt": 0.03, "g": 10.0, "m": 1.0, "l": 1.8} - contexts = {k: context for k in keys} - return contexts - - def test_default_selector(self): - from carl.context.selection import RoundRobinSelector - - contexts = self.generate_contexts() - env = CARLPendulumEnv(contexts=contexts) - - env.reset() - self.assertEqual(type(env.context_selector), RoundRobinSelector) - self.assertEqual(env.context_selector.n_calls, 1) - - env.reset() - self.assertEqual(env.context_key, "b") - - def test_roundrobin_selector_init(self): - from carl.context.selection import RoundRobinSelector - - contexts = self.generate_contexts() - env = CARLPendulumEnv( - contexts=contexts, context_selector=RoundRobinSelector(contexts=contexts) - ) - self.assertEqual(type(env.context_selector), RoundRobinSelector) - - def test_random_selector_init(self): - from carl.context.selection import RandomSelector - - contexts = self.generate_contexts() - env = CARLPendulumEnv( - contexts=contexts, context_selector=RandomSelector(contexts=contexts) - ) - self.assertEqual(type(env.context_selector), RandomSelector) - - def test_random_selectorclass_init(self): - from carl.context.selection import RandomSelector - - contexts = self.generate_contexts() - env = CARLPendulumEnv(contexts=contexts, context_selector=RandomSelector) - self.assertEqual(type(env.context_selector), RandomSelector) - - def test_unknown_selector_init(self): - with self.assertRaises(ValueError): - contexts = self.generate_contexts() - _ = CARLPendulumEnv(contexts=contexts, context_selector="bork") - - def test_get_context_key(self): - contexts = self.generate_contexts() - env = CARLPendulumEnv(contexts=contexts) - self.assertEqual(env.context_key, None) - - -class TestContextSampler(unittest.TestCase): - def test_get_defaults(self): - from carl.context.sampling import get_default_context_and_bounds - - defaults, bounds = get_default_context_and_bounds(env_name="CARLPendulumEnv") - DEFAULT_CONTEXT = { - "max_speed": 8.0, - "dt": 0.05, - "g": 10.0, - "m": 1.0, - "l": 1.0, - "initial_angle_max": np.pi, - "initial_velocity_max": 1, - } - self.assertDictEqual(defaults, DEFAULT_CONTEXT) - - def test_sample_contexts(self): - from carl.context.sampling import sample_contexts - - contexts = sample_contexts( - env_name="CARLPendulumEnv", - context_feature_args=["l"], - num_contexts=1, - default_sample_std_percentage=0.0, - ) - self.assertEqual(contexts[0]["l"], 1) - - -class TestContextAugmentation(unittest.TestCase): - def test_gaussian_noise(self): - from carl.context.augmentation import add_gaussian_noise - - c = add_gaussian_noise(default_value=1, percentage_std=0) - self.assertEqual(c, 1) + self.assertEqual(len(obs["context"]), len(context)) + + def test_observation_emptycontext(self): + env = CARLPendulum(obs_context_features=[]) + state, info = env.reset() + self.assertEqual(len(state["context"]), 0) + + def test_observation_reducedcontext(self): + n = 3 + context_keys = list(CARLPendulum.get_default_context().keys())[:n] + env = CARLPendulum(obs_context_features=context_keys) + state, info = env.reset() + self.assertEqual(len(state["context"]), n) if __name__ == "__main__": diff --git a/test/test_context_sampler.py b/test/test_context_sampler.py new file mode 100644 index 00000000..111255ec --- /dev/null +++ b/test/test_context_sampler.py @@ -0,0 +1,54 @@ +import unittest + +from carl.context.context_space import ( + ContextSpace, + NormalFloatContextFeature, + UniformFloatContextFeature, +) +from carl.context.sampler import ContextSampler + +context_space_dict = { + "gravity": UniformFloatContextFeature( + "gravity", lower=1, upper=10, default_value=9.8 + ) +} +sample_dist = { + "gravity": NormalFloatContextFeature( + "gravity", mu=9.8, sigma=0.0, default_value=9.8 + ) +} + + +class TestContextSampler(unittest.TestCase): + def setUp(self) -> None: + self.cspace = ContextSpace(context_space_dict) + self.sampler = ContextSampler( + context_distributions=sample_dist, + context_space=ContextSpace(context_space_dict), + seed=0, + name="TestSampler", + ) + return super().setUp() + + def test_init(self): + ContextSampler( + context_distributions=sample_dist, # as dict + context_space=self.cspace, + seed=0, + name="TestSampler", + ) + ContextSampler( + context_distributions=list(sample_dist.values()), # as list/iterable + context_space=self.cspace, + seed=0, + name="TestSampler", + ) + + def test_sample_contexts(self): + contexts = self.sampler.sample_contexts(n_contexts=3) + self.assertEqual(len(contexts), 3) + self.assertEqual(contexts[0]["gravity"], 9.8) + + +if __name__ == "__main__": + unittest.main() diff --git a/test/test_context_selector.py b/test/test_context_selector.py new file mode 100644 index 00000000..3e135ba8 --- /dev/null +++ b/test/test_context_selector.py @@ -0,0 +1,60 @@ +from typing import Any, Dict + +import unittest + +from carl.envs.gymnasium.classic_control.carl_pendulum import CARLPendulum +from carl.utils.types import Context + +CARLPendulum.render_mode = "rgb_array" + + +class TestContextSelection(unittest.TestCase): + @staticmethod + def generate_contexts() -> Dict[Any, Context]: + keys = "abc" + context = {"dt": 0.03, "gravity": 10.0, "m": 1.0, "l": 1.8} + contexts = {k: context for k in keys} + return contexts + + def test_default_selector(self): + from carl.context.selection import RoundRobinSelector + + contexts = self.generate_contexts() + env = CARLPendulum(contexts=contexts) + + env.reset() + self.assertEqual(type(env.context_selector), RoundRobinSelector) + self.assertEqual(env.context_selector.n_calls, 1) + + env.reset() + self.assertEqual(env.context_selector.n_calls, 2) + + def test_roundrobin_selector_init(self): + from carl.context.selection import RoundRobinSelector + + contexts = self.generate_contexts() + env = CARLPendulum( + contexts=contexts, context_selector=RoundRobinSelector(contexts=contexts) + ) + self.assertEqual(type(env.context_selector), RoundRobinSelector) + + def test_random_selector_init(self): + from carl.context.selection import RandomSelector + + contexts = self.generate_contexts() + env = CARLPendulum( + contexts=contexts, context_selector=RandomSelector(contexts=contexts) + ) + self.assertEqual(type(env.context_selector), RandomSelector) + + def test_random_selectorclass_init(self): + from carl.context.selection import RandomSelector + + contexts = self.generate_contexts() + env = CARLPendulum(contexts=contexts, context_selector=RandomSelector) + self.assertEqual(type(env.context_selector), RandomSelector) + + def test_unknown_selector_init(self): + with self.assertRaises(ValueError): + contexts = self.generate_contexts() + _ = CARLPendulum(contexts=contexts, context_selector="bork") diff --git a/test/test_context_space.py b/test/test_context_space.py new file mode 100644 index 00000000..00478679 --- /dev/null +++ b/test/test_context_space.py @@ -0,0 +1,93 @@ +import unittest + +import gymnasium +import numpy as np + +from carl.context.context_space import ( + ContextSpace, + UniformFloatContextFeature, + UniformIntegerContextFeature, +) + +context_space_dict = { + "gravity": UniformFloatContextFeature( + "gravity", lower=0.1, upper=np.inf, default_value=9.8 + ), + "masscart": UniformFloatContextFeature( + "masscart", lower=0.1, upper=10, default_value=1.0 + ), + "masspole": UniformFloatContextFeature( + "masspole", lower=0.01, upper=1, default_value=0.1 + ), + "length": UniformFloatContextFeature( + "length", lower=0.05, upper=5, default_value=0.5 + ), + "force_mag": UniformFloatContextFeature( + "force_mag", lower=1, upper=100, default_value=10.0 + ), + "tau": UniformFloatContextFeature( + "tau", lower=0.002, upper=0.2, default_value=0.02 + ), +} + +context_space_dict_othertypes = { + "gravity": UniformFloatContextFeature( + "gravity", lower=0.1, upper=np.inf, default_value=9.8 + ), + "masscart": UniformIntegerContextFeature( + "masscart", lower=1, upper=10, default_value=1 + ), +} + + +class TestContextSpace(unittest.TestCase): + def setUp(self) -> None: + self.default_context = { + "gravity": 9.8, + "masscart": 1, + "masspole": 0.1, + "length": 0.5, + "force_mag": 10, + "tau": 0.02, + } + self.context_space = ContextSpace(context_space=context_space_dict) + return super().setUp() + + def test_insert_defaults(self): + context_with_defaults = self.context_space.insert_defaults({}) + self.assertDictEqual(context_with_defaults, self.default_context) + + def test_get_default_context(self): + default_context = self.context_space.get_default_context() + self.assertDictEqual(default_context, self.default_context) + + def test_get_lower_and_upper_bound(self): + bounds_gt = (0.05, 5) + bounds = self.context_space.get_lower_and_upper_bound("length") + self.assertTupleEqual(bounds_gt, bounds) + + def test_to_gymnasium_space_type(self): + space = self.context_space.to_gymnasium_space(as_dict=False) + self.assertEqual(type(space), gymnasium.spaces.Box) + + space = self.context_space.to_gymnasium_space(as_dict=True) + self.assertEqual(type(space), gymnasium.spaces.Dict) + + def test_to_gynasium_space(self): + cspace = ContextSpace(context_space_dict_othertypes) + cspace.to_gymnasium_space() + + def test_verify_context(self): + # Unknown context feature name + context = {"hihi": 39, "gravity": 3} + is_valid = self.context_space.verify_context(context) + self.assertEqual(is_valid, False) + + # Out of bounds + context = {"masscart": -10} + is_valid = self.context_space.verify_context(context) + self.assertEqual(is_valid, False) + + +if __name__ == "__main__": + unittest.main() diff --git a/test/test_dmc.py b/test/test_dmc.py index 406f1b53..83a9c05c 100644 --- a/test/test_dmc.py +++ b/test/test_dmc.py @@ -43,12 +43,18 @@ def test_finger_constraints(self): # Finger can reach spinner? with self.assertRaises(ValueError): check_constraints( - limb_length_0=0.17, limb_length_1=0.16, spinner_length=0.1 + limb_length_0=0.17, + limb_length_1=0.16, + spinner_length=0.1, + raise_error=True, ) # Spinner collides with finger hinge? with self.assertRaises(ValueError): check_constraints( - limb_length_0=0.17, limb_length_1=0.16, spinner_length=0.81 + limb_length_0=0.17, + limb_length_1=0.16, + spinner_length=0.81, + raise_error=True, ) def test_finger_tasks(self): @@ -61,11 +67,11 @@ def test_finger_tasks(self): class TestDmcUtils(unittest.TestCase): def setUp(self) -> None: - from carl.envs.dmc.carl_dm_finger import DEFAULT_CONTEXT + from carl.envs.dmc.carl_dm_finger import CARLDmcFingerEnv from carl.envs.dmc.dmc_tasks.finger import get_model_and_assets self.xml_string, _ = get_model_and_assets() - self.default_context = DEFAULT_CONTEXT + self.default_context = CARLDmcFingerEnv.get_default_context() def test_adapt_context_no_context(self): context = {} @@ -81,25 +87,12 @@ def test_adapt_context_fullcontext(self): context["gravity"] *= 1.25 _ = adapt_context(xml_string=self.xml_string, context=context) - def test_adapt_context_contextmask(self): - # only continuous context features - context = self.default_context - context_mask = list(context.keys()) - _ = adapt_context( - xml_string=self.xml_string, context=context, context_mask=context_mask - ) - - def test_adapt_context_wind(self): - context = {"wind": 10} - with self.assertRaises(KeyError): - _ = adapt_context(xml_string=self.xml_string, context=context) - def test_adapt_context_friction(self): - from carl.envs.dmc.carl_dm_walker import DEFAULT_CONTEXT + from carl.envs.dmc.carl_dm_walker import CARLDmcWalkerEnv from carl.envs.dmc.dmc_tasks.walker import get_model_and_assets xml_string, _ = get_model_and_assets() - context = DEFAULT_CONTEXT + context = CARLDmcWalkerEnv.get_default_context() context["friction_tangential"] *= 1.3 _ = adapt_context(xml_string=xml_string, context=context) diff --git a/test/test_gymnasium_envs.py b/test/test_gymnasium_envs.py new file mode 100644 index 00000000..5be182c2 --- /dev/null +++ b/test/test_gymnasium_envs.py @@ -0,0 +1,25 @@ +import inspect +import unittest + +import carl.envs.gymnasium + + +class TestGymnasiumEnvs(unittest.TestCase): + def test_envs(self): + envs = inspect.getmembers(carl.envs.gymnasium) + + for env_name, env_obj in envs: + if inspect.isclass(env_obj) and "CARL" in env_name: + try: + env_obj.get_context_features() + + env = env_obj() + env._progress_instance() + env._update_context() + except Exception as e: + print(f"Cannot instantiate {env_name} environment.") + raise e + + +if __name__ == "__main__": + TestGymnasiumEnvs().test_envs() diff --git a/test/test_selector.py b/test/test_selector.py deleted file mode 100644 index ae52c84d..00000000 --- a/test/test_selector.py +++ /dev/null @@ -1,86 +0,0 @@ -from typing import Any, Dict - -import unittest -from unittest.mock import patch - -from carl.context.selection import ( - AbstractSelector, - CustomSelector, - RandomSelector, - RoundRobinSelector, -) -from carl.utils.types import Context - - -def dummy_select(dummy): - return None, None - - -class TestSelectors(unittest.TestCase): - @staticmethod - def generate_contexts() -> Dict[Any, Context]: - n_context_features = 5 - keys = "abc" - values = {str(i): i for i in range(n_context_features)} - contexts = {k: v for k, v in zip(keys, values)} - return contexts - - @patch.object(AbstractSelector, "_select", dummy_select) - def test_abstract_selector(self): - contexts = self.generate_contexts() - selector = AbstractSelector(contexts=contexts) - selector.select() - selector.select() - selector.select() - selector.select() - self.assertEqual(selector.n_calls, 4) - - def test_random_selector(self): - contexts = self.generate_contexts() - selector = RandomSelector(contexts=contexts) - selector.select() - selector.select() - selector.select() - - def test_roundrobin_selector(self): - contexts = self.generate_contexts() - selector = RoundRobinSelector(contexts=contexts) - - self.assertEqual(None, selector.context_id) - - selector.select() - self.assertEqual(selector.context_id, 0) - self.assertEqual(selector.contexts_keys[selector.context_id], "a") - - selector.select() - self.assertEqual(selector.context_id, 1) - self.assertEqual(selector.contexts_keys[selector.context_id], "b") - - selector.select() - self.assertEqual(selector.context_id, 2) - self.assertEqual(selector.contexts_keys[selector.context_id], "c") - - selector.select() - self.assertEqual(selector.context_id, 0) - self.assertEqual(selector.contexts_keys[selector.context_id], "a") - - def test_custom_selector(self): - def selector_function(inst: AbstractSelector): - if inst.n_calls == 0: - context_id = 1 - else: - context_id = 0 - return inst.contexts[inst.contexts_keys[context_id]], context_id - - contexts = self.generate_contexts() - selector = CustomSelector( - contexts=contexts, selector_function=selector_function - ) - - selector.select() - self.assertEqual(selector.context_id, 1) - self.assertEqual(selector.contexts_keys[selector.context_id], "b") - - selector.select() - self.assertEqual(selector.context_id, 0) - self.assertEqual(selector.contexts_keys[selector.context_id], "a")