diff --git a/_static/img/rollout_recurrent.png b/_static/img/rollout_recurrent.png new file mode 100644 index 0000000000..2ce24d40d2 Binary files /dev/null and b/_static/img/rollout_recurrent.png differ diff --git a/beginner_source/introyt/tensorboardyt_tutorial.py b/beginner_source/introyt/tensorboardyt_tutorial.py index 29e8306672..146747410a 100644 --- a/beginner_source/introyt/tensorboardyt_tutorial.py +++ b/beginner_source/introyt/tensorboardyt_tutorial.py @@ -214,13 +214,14 @@ def forward(self, x): # Check against the validation set running_vloss = 0.0 - net.train(False) # Don't need to track gradents for validation + # In evaluation mode some model specific operations can be omitted eg. dropout layer + net.train(False) # Switching to evaluation mode, eg. turning off regularisation for j, vdata in enumerate(validation_loader, 0): vinputs, vlabels = vdata voutputs = net(vinputs) vloss = criterion(voutputs, vlabels) running_vloss += vloss.item() - net.train(True) # Turn gradients back on for training + net.train(True) # Switching back to training mode, eg. turning on regularisation avg_loss = running_loss / 1000 avg_vloss = running_vloss / len(validation_loader) diff --git a/en-wordlist.txt b/en-wordlist.txt index cf2390040d..18ba658a33 100644 --- a/en-wordlist.txt +++ b/en-wordlist.txt @@ -62,6 +62,7 @@ Colab Conv ConvNet ConvNets +customizable DCGAN DCGANs DDP diff --git a/index.rst b/index.rst index fb2ce3bd6f..4c798e1f1c 100644 --- a/index.rst +++ b/index.rst @@ -312,6 +312,13 @@ What's new in PyTorch tutorials? :link: intermediate/mario_rl_tutorial.html :tags: Reinforcement-Learning +.. customcarditem:: + :header: Recurrent DQN + :card_description: Use TorchRL to train recurrent policies + :image: _static/img/rollout_recurrent.png + :link: intermediate/dqn_with_rnn_tutorial.html + :tags: Reinforcement-Learning + .. customcarditem:: :header: Code a DDPG Loss :card_description: Use TorchRL to code a DDPG Loss @@ -319,8 +326,6 @@ What's new in PyTorch tutorials? :link: advanced/coding_ddpg.html :tags: Reinforcement-Learning - - .. Deploying PyTorch Models in Production diff --git a/intermediate_source/dqn_with_rnn_tutorial.py b/intermediate_source/dqn_with_rnn_tutorial.py new file mode 100644 index 0000000000..55afbbe5e4 --- /dev/null +++ b/intermediate_source/dqn_with_rnn_tutorial.py @@ -0,0 +1,441 @@ +# -*- coding: utf-8 -*- + +""" +Recurrent DQN: Training recurrent policies +========================================== + +**Author**: `Vincent Moens <https://github.com/vmoens>`_ + +.. grid:: 2 + + .. grid-item-card:: :octicon:`mortar-board;1em;` What you will learn + + * How to incorporating an RNN in an actor in TorchRL + * How to use that memory-based policy with a replay buffer and a loss module + + .. grid-item-card:: :octicon:`list-unordered;1em;` Prerequisites + + * PyTorch v2.0.0 + * gym[mujoco] + * tqdm +""" + +######################################################################### +# Overview +# -------- +# +# Memory-based policies are crucial not only when the observations are partially +# observable but also when the time dimension must be taken into account to +# make informed decisions. +# +# Recurrent neural network have long been a popular tool for memory-based +# policies. The idea is to keep a recurrent state in memory between two +# consecutive steps, and use this as an input to the policy along with the +# current observation. +# +# This tutorial shows how to incorporate an RNN in a policy using TorchRL. +# +# Key learnings: +# +# - Incorporating an RNN in an actor in TorchRL; +# - Using that memory-based policy with a replay buffer and a loss module. +# +# The core idea of using RNNs in TorchRL is to use TensorDict as a data carrier +# for the hidden states from one step to another. We'll build a policy that +# reads the previous recurrent state from the current TensorDict, and writes the +# current recurrent states in the TensorDict of the next state: +# +# .. figure:: /_static/img/rollout_recurrent.png +# :alt: Data collection with a recurrent policy +# +# As this figure shows, our environment populates the TensorDict with zeroed recurrent +# states which are read by the policy together with the observation to produce an +# action, and recurrent states that will be used for the next step. +# When the :func:`~torchrl.envs.utils.step_mdp` function is called, the recurrent states +# from the next state are brought to the current TensorDict. Let's see how this +# is implemented in practice. + +###################################################################### +# If you are running this in Google Colab, make sure you install the following dependencies: +# +# .. code-block:: bash +# +# !pip3 install torchrl +# !pip3 install gym[mujoco] +# !pip3 install tqdm +# +# Setup +# ----- +# + +import torch +import tqdm +from tensordict.nn import TensorDictModule as Mod, TensorDictSequential as Seq +from torch import nn +from torchrl.collectors import SyncDataCollector +from torchrl.data import LazyMemmapStorage, TensorDictReplayBuffer +from torchrl.envs import ( + Compose, + ExplorationType, + GrayScale, + InitTracker, + ObservationNorm, + Resize, + RewardScaling, + set_exploration_type, + StepCounter, + ToTensorImage, + TransformedEnv, +) +from torchrl.envs.libs.gym import GymEnv +from torchrl.modules import ConvNet, EGreedyWrapper, LSTMModule, MLP, QValueModule +from torchrl.objectives import DQNLoss, SoftUpdate + +device = torch.device(0) if torch.cuda.device_count() else torch.device("cpu") + +###################################################################### +# Environment +# ----------- +# +# As usual, the first step is to build our environment: it helps us +# define the problem and build the policy network accordingly. For this tutorial, +# we'll be running a single pixel-based instance of the CartPole gym +# environment with some custom transforms: turning to grayscale, resizing to +# 84x84, scaling down the rewards and normalizing the observations. +# +# .. note:: +# The :class:`~torchrl.envs.transforms.StepCounter` transform is accessory. Since the CartPole +# task goal is to make trajectories as long as possible, counting the steps +# can help us track the performance of our policy. +# +# Two transforms are important for the purpose of this tutorial: +# +# - :class:`~torchrl.envs.transforms.InitTracker` will stamp the +# calls to :meth:`~torchrl.envs.EnvBase.reset` by adding a ``"is_init"`` +# boolean mask in the TensorDict that will track which steps require a reset +# of the RNN hidden states. +# - The :class:`~torchrl.envs.transforms.TensorDictPrimer` transform is a bit more +# technical. It is not required to use RNN policies. However, it +# instructs the environment (and subsequently the collector) that some extra +# keys are to be expected. Once added, a call to `env.reset()` will populate +# the entries indicated in the primer with zeroed tensors. Knowing that +# these tensors are expected by the policy, the collector will pass them on +# during collection. Eventually, we'll be storing our hidden states in the +# replay buffer, which will help us bootstrap the computation of the +# RNN operations in the loss module (which would otherwise be initiated +# with 0s). In summary: not including this transform will not impact hugely +# the training of our policy, but it will make the recurrent keys disappear +# from the collected data and the replay buffer, which will in turn lead to +# a slightly less optimal training. +# Fortunately, the :class:`~torchrl.modules.LSTMModule` we propose is +# equipped with a helper method to build just that transform for us, so +# we can wait until we build it! +# + +env = TransformedEnv( + GymEnv("CartPole-v1", from_pixels=True, device=device), + Compose( + ToTensorImage(), + GrayScale(), + Resize(84, 84), + StepCounter(), + InitTracker(), + RewardScaling(loc=0.0, scale=0.1), + ObservationNorm(standard_normal=True, in_keys=["pixels"]), + ), +) + +###################################################################### +# As always, we need to initialize manually our normalization constants: +# +env.transform[-1].init_stats(1000, reduce_dim=[0, 1, 2], cat_dim=0, keep_dims=[0]) +td = env.reset() + +###################################################################### +# Policy +# ------ +# +# Our policy will have 3 components: a :class:`~torchrl.modules.ConvNet` +# backbone, an :class:`~torchrl.modules.LSTMModule` memory layer and a shallow +# :class:`~torchrl.modules.MLP` block that will map the LSTM output onto the +# action values. +# +# Convolutional network +# ~~~~~~~~~~~~~~~~~~~~~ +# +# We build a convolutional network flanked with a :class:`torch.nn.AdaptiveAvgPool2d` +# that will squash the output in a vector of size 64. The :class:`~torchrl.modules.ConvNet` +# can assist us with this: +# + +feature = Mod( + ConvNet( + num_cells=[32, 32, 64], + squeeze_output=True, + aggregator_class=nn.AdaptiveAvgPool2d, + aggregator_kwargs={"output_size": (1, 1)}, + device=device, + ), + in_keys=["pixels"], + out_keys=["embed"], +) +###################################################################### +# we execute the first module on a batch of data to gather the size of the +# output vector: +# +n_cells = feature(env.reset())["embed"].shape[-1] + +###################################################################### +# LSTM Module +# ~~~~~~~~~~~ +# +# TorchRL provides a specialized :class:`~torchrl.modules.LSTMModule` class +# to incorporate LSTMs in your code-base. It is a :class:`~tensordict.nn.TensorDictModuleBase` +# subclass: as such, it has a set of ``in_keys`` and ``out_keys`` that indicate +# what values should be expected to be read and written/updated during the +# execution of the module. The class comes with customizable predefined +# values for these attributes to facilitate its construction. +# +# .. note:: +# *Usage limitations*: The class supports almost all LSTM features such as +# dropout or multi-layered LSTMs. +# However, to respect TorchRL's conventions, this LSTM must have the ``batch_first`` +# attribute set to ``True`` which is **not** the default in PyTorch. However, +# our :class:`~torchrl.modules.LSTMModule` changes this default +# behavior, so we're good with a native call. +# +# Also, the LSTM cannot have a ``bidirectional`` attribute set to ``True`` as +# this wouldn't be usable in online settings. In this case, the default value +# is the correct one. +# + +lstm = LSTMModule( + input_size=n_cells, + hidden_size=128, + device=device, + in_key="embed", + out_key="embed", +) + +###################################################################### +# Let us look at the LSTM Module class, specifically its in and out_keys: +print("in_keys", lstm.in_keys) +print("out_keys", lstm.out_keys) + +###################################################################### +# We can see that these values contain the key we indicated as the in_key (and out_key) +# as well as recurrent key names. The out_keys are preceded by a "next" prefix +# that indicates that they will need to be written in the "next" TensorDict. +# We use this convention (which can be overridden by passing the in_keys/out_keys +# arguments) to make sure that a call to :func:`~torchrl.envs.utils.step_mdp` will +# move the recurrent state to the root TensorDict, making it available to the +# RNN during the following call (see figure in the intro). +# +# As mentioned earlier, we have one more optional transform to add to our +# environment to make sure that the recurrent states are passed to the buffer. +# The :meth:`~torchrl.modules.LSTMModule.make_tensordict_primer` method does +# exactly that: +# +env.append_transform(lstm.make_tensordict_primer()) + +###################################################################### +# and that's it! We can print the environment to check that everything looks good now +# that we have added the primer: +print(env) + +###################################################################### +# MLP +# ~~~ +# +# We use a single-layer MLP to represent the action values we'll be using for +# our policy. +# +mlp = MLP( + out_features=2, + num_cells=[ + 64, + ], + device=device, +) +###################################################################### +# and fill the bias with zeros: + +mlp[-1].bias.data.fill_(0.0) +mlp = Mod(mlp, in_keys=["embed"], out_keys=["action_value"]) + +###################################################################### +# Using the Q-Values to select an action +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# +# The last part of our policy is the Q-Value Module. +# The Q-Value module :class:`~torchrl.modules.tensordict_module.QValueModule` +# will read the ``"action_values"`` key that is produced by our MLP and +# from it, gather the action that has the maximum value. +# The only thing we need to do is to specify the action space, which can be done +# either by passing a string or an action-spec. This allows us to use +# Categorical (sometimes called "sparse") encoding or the one-hot version of it. +# +qval = QValueModule(action_space=env.action_spec) + +###################################################################### +# .. note:: +# TorchRL also provides a wrapper class :class:`torchrl.modules.QValueActor` that +# wraps a module in a Sequential together with a :class:`~torchrl.modules.tensordict_module.QValueModule` +# like we are doing explicitly here. There is little advantage to do this +# and the process is less transparent, but the end results will be similar to +# what we do here. +# +# We can now put things together in a :class:`~tensordict.nn.TensorDictSequential` +# +stoch_policy = Seq(feature, lstm, mlp, qval) + +###################################################################### +# DQN being a deterministic algorithm, exploration is a crucial part of it. +# We'll be using an :math:`\epsilon`-greedy policy with an epsilon of 0.2 decaying +# progressively to 0. +# This decay is achieved via a call to :meth:`~torchrl.modules.EGreedyWrapper.step` +# (see training loop below). +# +stoch_policy = EGreedyWrapper( + stoch_policy, annealing_num_steps=1_000_000, spec=env.action_spec, eps_init=0.2 +) + +###################################################################### +# Using the model for the loss +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# +# The model as we've built it is well equipped to be used in sequential settings. +# However, the class :class:`torch.nn.LSTM` can use a cuDNN-optimized backend +# to run the RNN sequence faster on GPU device. We would not want to miss +# such an opportunity to speed up our training loop! +# To use it, we just need to tell the LSTM module to run on "recurrent-mode" +# when used by the loss. +# As we'll usually want to have two copies of the LSTM module, we do this by +# calling a :meth:`~torchrl.modules.LSTMModule.set_recurrent_mode` method that +# will return a new instance of the LSTM (with shared weights) that will +# assume that the input data is sequential in nature. +# +policy = Seq(feature, lstm.set_recurrent_mode(True), mlp, qval) + +###################################################################### +# Because we still have a couple of uninitialized parameters we should +# initialize them before creating an optimizer and such. +# +policy(env.reset()) + +###################################################################### +# DQN Loss +# -------- +# +# Out DQN loss requires us to pass the policy and, again, the action-space. +# While this may seem redundant, it is important as we want to make sure that +# the :class:`~torchrl.objectives.DQNLoss` and the :class:`~torchrl.modules.tensordict_module.QValueModule` +# classes are compatible, but aren't strongly dependent on each other. +# +# To use the Double-DQN, we ask for a ``delay_value`` argument that will +# create a non-differentiable copy of the network parameters to be used +# as a target network. +loss_fn = DQNLoss(policy, action_space=env.action_spec, delay_value=True) + +###################################################################### +# Since we are using a double DQN, we need to update the target parameters. +# We'll use a :class:`~torchrl.objectives.SoftUpdate` instance to carry out +# this work. +# +updater = SoftUpdate(loss_fn, eps=0.95) + +optim = torch.optim.Adam(policy.parameters(), lr=3e-4) + +###################################################################### +# Collector and replay buffer +# --------------------------- +# +# We build the simplest data collector there is. We'll try to train our algorithm +# with a million frames, extending the buffer with 50 frames at a time. The buffer +# will be designed to store 20 thousands trajectories of 50 steps each. +# At each optimization step (16 per data collection), we'll collect 4 items +# from our buffer, for a total of 200 transitions. +# We'll use a :class:`~torchrl.data.replay_buffers.LazyMemmapStorage` storage to keep the data +# on disk. +# +# .. note:: +# For the sake of efficiency, we're only running a few thousands iterations +# here. In a real setting, the total number of frames should be set to 1M. +# +collector = SyncDataCollector(env, stoch_policy, frames_per_batch=50, total_frames=200) +rb = TensorDictReplayBuffer( + storage=LazyMemmapStorage(20_000), batch_size=4, prefetch=10 +) + +###################################################################### +# Training loop +# ------------- +# +# To keep track of the progress, we will run the policy in the environment once +# every 50 data collection, and plot the results after training. +# + +utd = 16 +pbar = tqdm.tqdm(total=1_000_000) +longest = 0 + +traj_lens = [] +for i, data in enumerate(collector): + if i == 0: + print( + "Let us print the first batch of data.\nPay attention to the key names " + "which will reflect what can be found in this data structure, in particular: " + "the output of the QValueModule (action_values, action and chosen_action_value)," + "the 'is_init' key that will tell us if a step is initial or not, and the " + "recurrent_state keys.\n", + data, + ) + pbar.update(data.numel()) + # it is important to pass data that is not flattened + rb.extend(data.unsqueeze(0).to_tensordict().cpu()) + for _ in range(utd): + s = rb.sample().to(device, non_blocking=True) + loss_vals = loss_fn(s) + loss_vals["loss"].backward() + optim.step() + optim.zero_grad() + longest = max(longest, data["step_count"].max().item()) + pbar.set_description( + f"steps: {longest}, loss_val: {loss_vals['loss'].item(): 4.4f}, action_spread: {data['action'].sum(0)}" + ) + stoch_policy.step(data.numel()) + updater.step() + + with set_exploration_type(ExplorationType.MODE), torch.no_grad(): + rollout = env.rollout(10000, stoch_policy) + traj_lens.append(rollout.get(("next", "step_count")).max().item()) + +###################################################################### +# Let's plot our results: +# +if traj_lens: + from matplotlib import pyplot as plt + + plt.plot(traj_lens) + plt.xlabel("Test collection") + plt.title("Test trajectory lengths") + +###################################################################### +# Conclusion +# ---------- +# +# We have seen how an RNN can be incorporated in a policy in TorchRL. +# You should now be able: +# +# - Create an LSTM module that acts as a :class:`~tensordict.nn.TensorDictModule` +# - Indicate to the LSTM module that a reset is needed via an :class:`~torchrl.envs.transforms.InitTracker` +# transform +# - Incorporate this module in a policy and in a loss module +# - Make sure that the collector is made aware of the recurrent state entries +# such that they can be stored in the replay buffer along with the rest of +# the data +# +# Further Reading +# --------------- +# +# - The TorchRL documentation can be found `here <https://pytorch.org/rl/>`_. diff --git a/intermediate_source/mario_rl_tutorial.py b/intermediate_source/mario_rl_tutorial.py index ffb4a54ac0..67d50b121d 100755 --- a/intermediate_source/mario_rl_tutorial.py +++ b/intermediate_source/mario_rl_tutorial.py @@ -199,7 +199,7 @@ def __init__(self, env, shape): def observation(self, observation): transforms = T.Compose( - [T.Resize(self.shape), T.Normalize(0, 255)] + [T.Resize(self.shape, antialias=True), T.Normalize(0, 255)] ) observation = transforms(observation).squeeze(0) return observation diff --git a/requirements.txt b/requirements.txt index 3079989468..36b5945380 100644 --- a/requirements.txt +++ b/requirements.txt @@ -25,8 +25,8 @@ tensorboard jinja2==3.0.3 pytorch-lightning torchx -torchrl==0.2.0 -tensordict==0.2.0 +torchrl==0.2.1 +tensordict==0.2.1 ax-platform nbformat>=4.2.0 datasets