Subgoal Search For Complex Reasoning Tasks

Requirements

Source code

Clone the repository:

git clone https://github.com/subgoal-search/subgoal-search.git

All commands from sections Training and Evaluation should be called from the root directory of the repository.

Project resources

Download and unzip Google Drive directory containing all project resources: link. You can do it via Google's graphical interface or using gdrive CLI tool.

It is recommended to set environmental variable KSUBS_RESOURCES to the path with unzipped directory:

export KSUBS_RESOURCES="/path/to/the/root/dir/of/resources"

which is frequently used in commands below (alternatively you may replace it on your own for each command separately).

Resources include:

Singularity image
Pretrained network checkpoints
Datasets for Sokoban

Dependencies

Singularity (recommended)

Python interpreter and all required libraries are installed in the singularity image – singularity_image.sif file in resources directory. It is the easiest and recommended way of environment setup. Singularity is a docker-like container platform, commonly supported in computing centers. You can also install it on your own machine if needed – see linked documentation.

Alternatively, one can also build mentioned singularity image from scratch using singularity.def file from this repository (it may take a while - around 30-60 min).

sudo singularity build singularity_image.sif singularity.def

To run a command from Training or Evaluation section inside a singularity container, prepend the command with the following prefix:

singularity exec -B "${KSUBS_RESOURCES}" --nv "${KSUBS_RESOURCES}/singularity_image.sif"

So for example to run Rubik's Cube baseline evaluation, you need to run the following command:

singularity exec -B "${KSUBS_RESOURCES}" --nv "${KSUBS_RESOURCES}/singularity_image.sif" \
python3 runner.py \
--config_file="configs/rubik/solve/baseline.gin" \
--config="ValueEstimatorRubik.checkpoint_path=\"${KSUBS_RESOURCES}/rubik/rubik_value\"" \
--config="VanillaPolicyRubik.checkpoint_path=\"${KSUBS_RESOURCES}/rubik/rubik_vanilla_policy\""

Virtualenv/conda (fall-back option)

If singularity is not an option in your computing setup, you may use classic virtualenv or conda environment instead. Just create environment with Python 3.6.9 and install required dependencies:

pip3 install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html

Libraries and versions specified in requirements.txt match their counterparts from singularity image. Everything should work as with singularity image, but bear in mind that most of the commands were tested in singularity setup and virtualenv/conda setup is only a fall-back option.

Training

To train models mentioned in the paper, one can use commands listed below.

INT

Proof length 5

Subgoal generator (k = 2)
Requires: 16GB RAM, 1 GPU.

python3 runner.py \
--config_file="configs/int/train/proof_len_5/ksubs/subgoal_generator.gin"

Conditional policy (k = 2)
Requires: 16GB RAM, 1 GPU.

python3 runner.py \
--config_file="configs/int/train/proof_len_5/ksubs/conditional_policy.gin"

Policy
Requires: 16GB RAM, 1 GPU.

python3 runner.py \
--config_file="configs/int/train/proof_len_5/baseline/policy.gin"

Value network
Requires: 16GB RAM, 1 GPU.

python3 runner.py \
--config_file="configs/int/train/proof_len_5/value.gin"

Proof length 10

Subgoal generator (k = 3)
Requires: 16GB RAM, 1 GPU.

python3 runner.py \
--config_file="configs/int/train/proof_len_10/ksubs/subgoal_generator.gin"

Conditional policy (k = 3)
Requires: 16GB RAM, 1 GPU.

python3 runner.py \
--config_file="configs/int/train/proof_len_10/ksubs/conditional_policy.gin"

Policy
Requires: 16GB RAM, 1 GPU.

python3 runner.py \
--config_file="configs/int/train/proof_len_10/baseline/policy.gin"

Value network
Requires: 16GB RAM, 1 GPU.

python3 runner.py \
--config_file="configs/int/train/proof_len_10/value.gin"

Proof length 15

Subgoal generator (k = 3)
Requires: 16GB RAM, 1 GPU.

python3 runner.py \
--config_file="configs/int/train/proof_len_15/ksubs/subgoal_generator.gin"

Conditional policy (k = 3)
Requires: 16GB RAM, 1 GPU.

python3 runner.py \
--config_file="configs/int/train/proof_len_15/ksubs/conditional_policy.gin"

Policy
Requires: 16GB RAM, 1 GPU.

python3 runner.py \
--config_file="configs/int/train/proof_len_15/baseline/policy.gin"

Value network
Requires: 16GB RAM, 1 GPU.

python3 runner.py \
--config_file="configs/int/train/proof_len_15/value.gin"

Sokoban

Subgoal generator
Requires: 120GB RAM, 28 CPU.

You can change size of board using Sokoban.dim_room parameter, but remember to adjust JobTrainSokobanPixelDiff.dataset as well.

k is configurable with JobTrainSokobanPixelDiff.steps_into_future parameter.

python3 runner.py \
--config_file="configs/sokoban/train/subgoal_generator.gin" \
--config="JobTrainSokobanPixelDiff.dataset=\"${KSUBS_RESOURCES}/sokoban/datasets/12-12-4/\"" \
--config="JobTrainSokobanPixelDiff.steps_into_future=4" \
--config="Sokoban.dim_room=(12,12)"

Baseline policy
Requires: 120GB RAM, 28 CPU.

You can change size of board JobTrainSokobanPixelDiff.dataset.

python3 runner.py \
--config_file="configs/sokoban/train/policy.gin" \
--config="JobSokobanTrainPolicyBaseline.dataset=\"${KSUBS_RESOURCES}/sokoban/datasets/12-12-4/\""

Rubik's Cube

Subgoal generator (k = 4)
Requires: 10GB RAM, 1 GPU.

python3 runner.py \
--config_file="configs/rubik/train/ksubs/subgoal_generator.gin"

Conditional policy (k = 4)
Requires: 10GB RAM, 1 GPU.

python3 runner.py \
--config_file="configs/rubik/train/ksubs/conditional_policy.gin"

Policy
Requires: 10GB RAM, 1 GPU.

python3 runner.py \
--config_file="configs/rubik/train/baseline/policy.gin"

Value network
Requires: 10GB RAM, 1 GPU.

python3 runner.py \
--config_file="configs/rubik/train/value.gin"

Evaluation

The following commands allow to run evaluation of kSubS and baselines presented in the paper. In our publication we evaluated each method on the 1000 randomly sampled problems. Here to allow faster execution, we set number of problems (n_jobs) to 5. To run a full evaluation and replicate results presented in the paper, remove the last line of each command in this section.

It is also recommended to redirect stdout and stderr to seperate files, as stderr contains much redundant information.

INT: Table 2

Proof length 5

BestFS (baseline)
Requires: 6GB RAM, 1 GPU.

python3 runner.py \
--config_file="configs/int/solve/bfs/baseline.gin" \
--config="generate_problems.proof_length=5" \
--config="VanillaPolicyINT.checkpoint_path=\"${KSUBS_RESOURCES}/int/proof_len_5/int_len_5_vanilla_policy\"" \
--config="ValueEstimatorINT.checkpoint_path=\"${KSUBS_RESOURCES}/int/proof_len_5/int_len_5_value\"" \
--config="JobSolveINT.n_jobs=5"

BF-kSubS (k = 2)
Requires: 6GB RAM, 1 GPU.

python3 runner.py \
--config_file="configs/int/solve/bfs/ksubs.gin" \
--config="generate_problems.proof_length=5" \
--config="GoalGeneratorINT.generator_checkpoint_path=\"${KSUBS_RESOURCES}/int/proof_len_5/int_len_5_generator_k_2\"" \
--config="ConditionalPolicyINT.checkpoint_path=\"${KSUBS_RESOURCES}/int/proof_len_5/int_len_5_conditional_policy_k_2\"" \
--config="ValueEstimatorINT.checkpoint_path=\"${KSUBS_RESOURCES}/int/proof_len_5/int_len_5_value\"" \
--config="JobSolveINT.n_jobs=5"

Proof length 10

BestFS (baseline)
Requires: 6GB RAM, 1 GPU.

python3 runner.py \
--config_file="configs/int/solve/bfs/baseline.gin" \
--config="generate_problems.proof_length=10" \
--config="VanillaPolicyINT.checkpoint_path=\"${KSUBS_RESOURCES}/int/proof_len_10/int_len_10_vanilla_policy\"" \
--config="ValueEstimatorINT.checkpoint_path=\"${KSUBS_RESOURCES}/int/proof_len_10/int_len_10_value\"" \
--config="JobSolveINT.n_jobs=5"

BF-kSubS (k = 3)
Requires: 6GB RAM, 1 GPU.

python3 runner.py \
--config_file="configs/int/solve/bfs/ksubs.gin" \
--config="generate_problems.proof_length=10" \
--config="GoalGeneratorINT.generator_checkpoint_path=\"${KSUBS_RESOURCES}/int/proof_len_10/int_len_10_generator_k_3\"" \
--config="ConditionalPolicyINT.checkpoint_path=\"${KSUBS_RESOURCES}/int/proof_len_10/int_len_10_conditional_policy_k_3\"" \
--config="ValueEstimatorINT.checkpoint_path=\"${KSUBS_RESOURCES}/int/proof_len_10/int_len_10_value\""  \
--config="JobSolveINT.n_jobs=5"

Proof length 15

BestFS (baseline)
Requires: 6GB RAM, 1 GPU.

python3 runner.py \
--config_file="configs/int/solve/bfs/baseline.gin" \
--config="generate_problems.proof_length=15" \
--config="VanillaPolicyINT.checkpoint_path=\"${KSUBS_RESOURCES}/int/proof_len_15/int_len_15_vanilla_policy\"" \
--config="ValueEstimatorINT.checkpoint_path=\"${KSUBS_RESOURCES}/int/proof_len_15/int_len_15_value\""  \
--config="JobSolveINT.n_jobs=5"

BF-kSubS (k = 3)
Requires: 6GB RAM, 1 GPU.

python3 runner.py \
--config_file="configs/int/solve/bfs/ksubs.gin" \
--config="generate_problems.proof_length=15" \
--config="GoalGeneratorINT.generator_checkpoint_path=\"${KSUBS_RESOURCES}/int/proof_len_15/int_len_15_generator_k_3\"" \
--config="ConditionalPolicyINT.checkpoint_path=\"${KSUBS_RESOURCES}/int/proof_len_15/int_len_15_conditional_policy_k_3\"" \
--config="ValueEstimatorINT.checkpoint_path=\"${KSUBS_RESOURCES}/int/proof_len_15/int_len_15_value\"" \
--config="JobSolveINT.n_jobs=5"

INT: Figure 1

BestFS

See commands in section INT: Table 2, Proof length 15.

MCTS

Number of MCTS planning passes is configurable with parameter StochasticMCTSAgent.n_passes.

MCTS (AlphaZero)
Requires: 6GB RAM, 1 GPU.

python3 runner.py \
--config_file="configs/int/solve/mcts/baseline.gin" \
--config="generate_problems.proof_length=15" \
--config="StochasticMCTSAgent.n_passes=5" \
--config="MCTSVanillaGoalBuilderInt.checkpoint_path=\"${KSUBS_RESOURCES}/int/proof_len_15/int_len_15_vanilla_policy\"" \
--config="JobMCTSSolveInt.value_estimator_checkpoint=\"${KSUBS_RESOURCES}/int/proof_len_15/int_len_15_value\"" \
--config="JobMCTSSolveInt.n_proofs=5"

MCTS-kSubS (k = 3)
Requires: 6GB RAM, 1 GPU.

python3 runner.py \
--config_file="configs/int/solve/mcts/ksubs.gin" \
--config="generate_problems.proof_length=15" \
--config="StochasticMCTSAgent.n_passes=5" \
--config="GoalGeneratorINT.generator_checkpoint_path=\"${KSUBS_RESOURCES}/int/proof_len_15/int_len_15_generator_k_3\"" \
--config="ConditionalPolicyINT.checkpoint_path=\"${KSUBS_RESOURCES}/int/proof_len_15/int_len_15_conditional_policy_k_3\"" \
--config="JobMCTSSolveInt.value_estimator_checkpoint=\"${KSUBS_RESOURCES}/int/proof_len_15/int_len_15_value\"" \
--config="JobMCTSSolveInt.n_proofs=5"

INT: Figure 3

To test generalization for longer proofs, run the following commands for different values of generate_problems.proof_length (lengths 10-14 in the paper).

BestFS
Requires: 6GB RAM, 1 GPU.

python3 runner.py \
--config_file="configs/int/solve/bfs/baseline.gin" \
--config="generate_problems.proof_length=12" \
--config="VanillaPolicyINT.checkpoint_path=\"${KSUBS_RESOURCES}/int/proof_len_10/int_len_10_vanilla_policy\"" \
--config="ValueEstimatorINT.checkpoint_path=\"${KSUBS_RESOURCES}/int/proof_len_10/int_len_10_value\"" \
--config="JobSolveINT.n_jobs=5"

BF-kSubS (k = 3)
Requires: 6GB RAM, 1 GPU.

python3 runner.py \
--config_file="configs/int/solve/bfs/ksubs.gin" \
--config="generate_problems.proof_length=12" \
--config="GoalGeneratorINT.generator_checkpoint_path=\"${KSUBS_RESOURCES}/int/proof_len_10/int_len_10_generator_k_3\"" \
--config="ConditionalPolicyINT.checkpoint_path=\"${KSUBS_RESOURCES}/int/proof_len_10/int_len_10_conditional_policy_k_3\"" \
--config="ValueEstimatorINT.checkpoint_path=\"${KSUBS_RESOURCES}/int/proof_len_10/int_len_10_value\"" \
--config="JobSolveINT.n_jobs=5"

Sokoban: Figure 1, Figure 2, Table 3

BestFS
Requires: 30GB RAM, 4 CPU.

python3 runner.py \
--config_file="configs/sokoban/solve/baseline.gin" \
--config="Sokoban.dim_room=(12,12)" \
--config="SokobanPolicyBaseline.model_id=\"${KSUBS_RESOURCES}/sokoban/policy/12-12-4\"" \
--config="ValueEstimator.model_id=\"${KSUBS_RESOURCES}/sokoban/value/12-12-4\""

BF-kSubS (k = 4)
Requires: 30GB RAM, 4 CPU.

BestFSSolverSokoban.max_steps should be equal to JobTrainSokobanPixelDiff.steps_into_future (k parameter) used for subgoal generator training.

python3 runner.py \
--config_file="configs/sokoban/solve/ksubs.gin" \
--config="BestFSSolverSokoban.max_steps=4" \
--config="Sokoban.dim_room=(12,12)" \
--config="GoalPredictorPixelDiff.model_id=\"${KSUBS_RESOURCES}/sokoban/subgoal_generator/12-12-4\"" \
--config="ValueEstimator.model_id=\"${KSUBS_RESOURCES}/sokoban/value/12-12-4\""

For both BestFS and BF-kSubS evaluation you can change size of board using Sokoban.dim_room parameter, but remember to adjust checkpoint paths as well.

Note that these are single thread evaluations. They can be speed up by increasing JobSolveSokoban.n_parallel_workers and JobSolveSokoban.batch_size in gin configs, but this requires more RAM and CPU.

Rubik's Cube: Figure 1.

BestFS
Requires: 10GB RAM, 20 CPU.

python3 runner.py \
--config_file="configs/rubik/solve/baseline.gin" \
--config="ValueEstimatorRubik.checkpoint_path=\"${KSUBS_RESOURCES}/rubik/rubik_value\"" \
--config="VanillaPolicyRubik.checkpoint_path=\"${KSUBS_RESOURCES}/rubik/rubik_vanilla_policy\"" \
--config="JobSolveRubik.n_jobs=5"

BF-kSubS (k = 4)
Requires: 10GB RAM, 20 CPU.

python3 runner.py \
--config_file="configs/rubik/solve/ksubs.gin" \
--config="GoalGeneratorRubik.generator_checkpoint_path=\"${KSUBS_RESOURCES}/rubik/rubik_generator_k_4\"" \
--config="ConditionalPolicyRubik.checkpoint_path=\"${KSUBS_RESOURCES}/rubik/rubik_conditional_policy_k_4\"" \
--config="ValueEstimatorRubik.checkpoint_path=\"${KSUBS_RESOURCES}/rubik/rubik_value\"" \
--config="JobSolveRubik.n_jobs=5"

Pre-trained Models

You can find pretrained models for all commands from this README inside in the resources directory mentioned in Requirements section.

Results

We present the main results below. For more results see the paper.

Figure 1: The performance of Subgoal Search.

(top, left) compared on INT (with the proof length 15) to AlphaZero.
(top, right) BF-kSubS consistently achieves high performance, even for small computational budgets.
(bottom, left) similarly on Sokoban (board size 12x12 with 4 boxes) the advantage of BF-kSubS is prominent for small budget.
(bottom, right) BestFS fails to solve Rubik’s Cube.

Name		Name	Last commit message	Last commit date
Latest commit History 17 Commits
alpacka		alpacka
configs		configs
custom		custom
envs		envs
goal_builders		goal_builders
goal_generating_networks		goal_generating_networks
graph_tracer		graph_tracer
jobs		jobs
mrunner_utils		mrunner_utils
policies		policies
solvers		solvers
supervised		supervised
third_party		third_party
utils		utils
value_estimators		value_estimators
.gitignore		.gitignore
README.md		README.md
internal_run.py		internal_run.py
metric_logging.py		metric_logging.py
pytest.ini		pytest.ini
requirements.txt		requirements.txt
singularity.def		singularity.def
sokoban_jobs_test.py		sokoban_jobs_test.py

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Subgoal Search For Complex Reasoning Tasks

Requirements

Source code

Project resources

Dependencies

Singularity (recommended)

Virtualenv/conda (fall-back option)

Training

INT

Proof length 5

Proof length 10

Proof length 15

Sokoban

Rubik's Cube

Evaluation

INT: Table 2

Proof length 5

Proof length 10

Proof length 15

INT: Figure 1

BestFS

MCTS

INT: Figure 3

Sokoban: Figure 1, Figure 2, Table 3

Rubik's Cube: Figure 1.

Pre-trained Models

Results

About

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Subgoal Search For Complex Reasoning Tasks

Requirements

Source code

Project resources

Dependencies

Singularity (recommended)

Virtualenv/conda (fall-back option)

Training

INT

Proof length 5

Proof length 10

Proof length 15

Sokoban

Rubik's Cube

Evaluation

INT: Table 2

Proof length 5

Proof length 10

Proof length 15

INT: Figure 1

BestFS

MCTS

INT: Figure 3

Sokoban: Figure 1, Figure 2, Table 3

Rubik's Cube: Figure 1.

Pre-trained Models

Results

About

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