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Documentation | TensorDict | Features | Examples, tutorials and demos | Running examples | Upcoming features | Contributing
TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch.
It provides pytorch and python-first, low and high level abstractions for RL that are intended to be efficient, modular, documented and properly tested. The code is aimed at supporting research in RL. Most of it is written in python in a highly modular way, such that researchers can easily swap components, transform them or write new ones with little effort.
This repo attempts to align with the existing pytorch ecosystem libraries in that it has a dataset pillar (torchrl/envs), transforms, models, data utilities (e.g. collectors and containers), etc. TorchRL aims at having as few dependencies as possible (python standard library, numpy and pytorch). Common environment libraries (e.g. OpenAI gym) are only optional.
On the low-level end, torchrl comes with a set of highly re-usable functionals for cost functions, returns and data processing.
TorchRL aims at (1) a high modularity and (2) good runtime performance.
The TorchRL documentation can be found here. It contains tutorials and the API reference.
TorchRL relies on TensorDict,
a convenient data structure(1) to pass data from
one object to another without friction.
TensorDict makes it easy to re-use pieces of code across environments, models and
algorithms. For instance, here's how to code a rollout in TorchRL:
Code
- obs, done = env.reset()
+ tensordict = env.reset()
policy = SafeModule(
model,
in_keys=["observation_pixels", "observation_vector"],
out_keys=["action"],
)
out = []
for i in range(n_steps):
- action, log_prob = policy(obs)
- next_obs, reward, done, info = env.step(action)
- out.append((obs, next_obs, action, log_prob, reward, done))
- obs = next_obs
+ tensordict = policy(tensordict)
+ tensordict = env.step(tensordict)
+ out.append(tensordict)
+ tensordict = step_mdp(tensordict) # renames next_observation_* keys to observation_*
- obs, next_obs, action, log_prob, reward, done = [torch.stack(vals, 0) for vals in zip(*out)]
+ out = torch.stack(out, 0) # TensorDict supports multiple tensor operationsCode
- for i, (obs, next_obs, action, hidden_state, reward, done) in enumerate(collector):
+ for i, tensordict in enumerate(collector):
- replay_buffer.add((obs, next_obs, action, log_prob, reward, done))
+ replay_buffer.add(tensordict)
for j in range(num_optim_steps):
- obs, next_obs, action, hidden_state, reward, done = replay_buffer.sample(batch_size)
- loss = loss_fn(obs, next_obs, action, hidden_state, reward, done)
+ tensordict = replay_buffer.sample(batch_size)
+ loss = loss_fn(tensordict)
loss.backward()
optim.step()
optim.zero_grad()Again, this training loop can be re-used across algorithms as it makes a minimal number of assumptions about the structure of the data.
TensorDict supports multiple tensor operations on its device and shape (the shape of TensorDict, or its batch size, is the common arbitrary N first dimensions of all its contained tensors):
Code
# stack and cat
tensordict = torch.stack(list_of_tensordicts, 0)
tensordict = torch.cat(list_of_tensordicts, 0)
# reshape
tensordict = tensordict.view(-1)
tensordict = tensordict.permute(0, 2, 1)
tensordict = tensordict.unsqueeze(-1)
tensordict = tensordict.squeeze(-1)
# indexing
tensordict = tensordict[:2]
tensordict[:, 2] = sub_tensordict
# device and memory location
tensordict.cuda()
tensordict.to("cuda:1")
tensordict.share_memory_()Check our TorchRL-specific TensorDict tutorial for more information.
The associated SafeModule class which is functorch-compatible!
Code
transformer_model = nn.Transformer(nhead=16, num_encoder_layers=12)
+ td_module = SafeModule(transformer_model, in_keys=["src", "tgt"], out_keys=["out"])
src = torch.rand((10, 32, 512))
tgt = torch.rand((20, 32, 512))
+ tensordict = TensorDict({"src": src, "tgt": tgt}, batch_size=[20, 32])
- out = transformer_model(src, tgt)
+ td_module(tensordict)
+ out = tensordict["out"]The SafeSequential class allows to branch sequences of nn.Module instances in a highly modular way.
For instance, here is an implementation of a transformer using the encoder and decoder blocks:
encoder_module = TransformerEncoder(...)
encoder = SafeModule(encoder_module, in_keys=["src", "src_mask"], out_keys=["memory"])
decoder_module = TransformerDecoder(...)
decoder = SafeModule(decoder_module, in_keys=["tgt", "memory"], out_keys=["output"])
transformer = SafeSequential(encoder, decoder)
assert transformer.in_keys == ["src", "src_mask", "tgt"]
assert transformer.out_keys == ["memory", "output"]SafeSequential allows to isolate subgraphs by querying a set of desired input / output keys:
transformer.select_subsequence(out_keys=["memory"]) # returns the encoder
transformer.select_subsequence(in_keys=["tgt", "memory"]) # returns the decoderThe corresponding tutorial provides more context about its features.
-
a generic trainer class(1) that executes the aforementioned training loop. Through a hooking mechanism, it also supports any logging or data transformation operation at any given time.
-
A common interface for environments which supports common libraries (OpenAI gym, deepmind control lab, etc.)(1) and state-less execution (e.g. Model-based environments). The batched environments containers allow parallel execution(2). A common pytorch-first class of tensor-specification class is also provided.
Code
env_make = lambda: GymEnv("Pendulum-v1", from_pixels=True) env_parallel = ParallelEnv(4, env_make) # creates 4 envs in parallel tensordict = env_parallel.rollout(max_steps=20, policy=None) # random rollout (no policy given) assert tensordict.shape == [4, 20] # 4 envs, 20 steps rollout env_parallel.action_spec.is_in(tensordict["action"]) # spec check returns True
-
multiprocess data collectors(2) that work synchronously or asynchronously. Through the use of TensorDict, TorchRL's training loops are made very similar to regular training loops in supervised learning (although the "dataloader" -- read data collector -- is modified on-the-fly):
Code
env_make = lambda: GymEnv("Pendulum-v1", from_pixels=True) collector = MultiaSyncDataCollector( [env_make, env_make], policy=policy, devices=["cuda:0", "cuda:0"], total_frames=10000, frames_per_batch=50, ... ) for i, tensordict_data in enumerate(collector): loss = loss_module(tensordict_data) loss.backward() optim.step() optim.zero_grad() collector.update_policy_weights_()
-
efficient(2) and generic(1) replay buffers with modularized storage:
Code
storage = LazyMemmapStorage( # memory-mapped (physical) storage cfg.buffer_size, scratch_dir="/tmp/" ) buffer = TensorDictPrioritizedReplayBuffer( alpha=0.7, beta=0.5, collate_fn=lambda x: x, pin_memory=device != torch.device("cpu"), prefetch=10, # multi-threaded sampling storage=storage )
-
cross-library environment transforms(1), executed on device and in a vectorized fashion(2), which process and prepare the data coming out of the environments to be used by the agent:
Code
env_make = lambda: GymEnv("Pendulum-v1", from_pixels=True) env_base = ParallelEnv(4, env_make, device="cuda:0") # creates 4 envs in parallel env = TransformedEnv( env_base, Compose( ToTensorImage(), ObservationNorm(loc=0.5, scale=1.0)), # executes the transforms once and on device ) tensordict = env.reset() assert tensordict.device == torch.device("cuda:0")
Other transforms include: reward scaling (
RewardScaling), shape operations (concatenation of tensors, unsqueezing etc.), contatenation of successive operations (CatFrames), resizing (Resize) and many more.Unlike other libraries, the transforms are stacked as a list (and not wrapped in each other), which makes it easy to add and remove them at will:
env.insert_transform(0, NoopResetEnv()) # inserts the NoopResetEnv transform at the index 0
Nevertheless, transforms can access and execute operations on the parent environment:
transform = env.transform[1] # gathers the second transform of the list parent_env = transform.parent # returns the base environment of the second transform, i.e. the base env + the first transform
-
various tools for distributed learning (e.g. memory mapped tensors)(2);
-
various architectures and models (e.g. actor-critic)(1):
Code
# create an nn.Module common_module = ConvNet( bias_last_layer=True, depth=None, num_cells=[32, 64, 64], kernel_sizes=[8, 4, 3], strides=[4, 2, 1], ) # Wrap it in a SafeModule, indicating what key to read in and where to # write out the output common_module = SafeModule( common_module, in_keys=["pixels"], out_keys=["hidden"], ) # Wrap the policy module in NormalParamsWrapper, such that the output # tensor is split in loc and scale, and scale is mapped onto a positive space policy_module = SafeModule( NormalParamsWrapper( MLP(num_cells=[64, 64], out_features=32, activation=nn.ELU) ), in_keys=["hidden"], out_keys=["loc", "scale"], ) # Use a SafeProbabilisticSequential to combine the SafeModule with a # SafeProbabilisticModule, indicating how to build the # torch.distribution.Distribution object and what to do with it policy_module = SafeProbabilisticSequential( # stochastic policy policy_module, SafeProbabilisticModule( in_keys=["loc", "scale"], out_keys="action", distribution_class=TanhNormal, ), ) value_module = MLP( num_cells=[64, 64], out_features=1, activation=nn.ELU, ) # Wrap the policy and value funciton in a common module actor_value = ActorValueOperator(common_module, policy_module, value_module) # standalone policy from this standalone_policy = actor_value.get_policy_operator()
-
exploration wrappers and modules to easily swap between exploration and exploitation(1):
Code
policy_explore = EGreedyWrapper(policy) with set_exploration_mode("random"): tensordict = policy_explore(tensordict) # will use eps-greedy with set_exploration_mode("mode"): tensordict = policy_explore(tensordict) # will not use eps-greedy
-
A series of efficient loss modules and highly vectorized functional return and advantage computation.
Code
from torchrl.objectives import DQNLoss loss_module = DQNLoss(value_network=value_network, gamma=0.99) tensordict = replay_buffer.sample(batch_size) loss = loss_module(tensordict)
from torchrl.objectives.value.functional import vec_td_lambda_return_estimate advantage = vec_td_lambda_return_estimate(gamma, lmbda, next_state_value, reward, done)
-
various recipes to build models that correspond to the environment being deployed.
If you feel a feature is missing from the library, please submit an issue! If you would like to contribute to new features, check our call for contributions and our contribution page.
A series of examples are provided with an illustrative purpose:
and many more to come!
We also provide tutorials and demos that give a sense of what the library can do.
Create a conda environment where the packages will be installed.
conda create --name torch_rl python=3.9
conda activate torch_rl
Depending on the use of functorch that you want to make, you may want to install the latest (nightly) pytorch release or the latest stable version of pytorch.
See here for a more detailed list of commands, including pip3 or windows/OSX compatible installation commands:
Stable
# For CUDA 11.3
conda install pytorch torchvision cudatoolkit=11.3 -c pytorch
# For CUDA 11.6
conda install pytorch torchvision torchaudio cudatoolkit=11.6 -c pytorch -c conda-forge
# For CPU-only build
conda install pytorch torchvision cpuonly -c pytorch
# For torch 1.12 (and not above), one should install functorch separately:
pip3 install functorch
Nightly
# For CUDA 11.6
conda install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch-nightly -c nvidia
# For CUDA 11.7
conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch-nightly -c nvidia
# For CPU-only build
conda install pytorch torchvision torchaudio cpuonly -c pytorch-nightly
functorch is included in the nightly PyTorch package, so no need to install it separately.
For M1 Mac users, if the above commands do not work, you can build torch from source by following this guide.
Torchrl
You can install the latest stable release by using
pip3 install torchrl
This should work on linux and MacOs (not M1). For Windows and M1/M2 machines, one should install the library locally (see below).
The nightly build can be installed via
pip install torchrl-nightly
To install extra dependencies, call
pip3 install "torchrl[atari,dm_control,gym_continuous,rendering,tests,utils]"
or a subset of these.
Alternatively, as the library is at an early stage, it may be wise to install it in develop mode as this will make it possible to pull the latest changes and benefit from them immediately. Start by cloning the repo:
git clone https://github.com/pytorch/rl
Go to the directory where you have cloned the torchrl repo and install it
cd /path/to/torchrl/
pip install -e .
On M1 machines, this should work out-of-the-box with the nightly build of PyTorch.
If the generation of this artifact in MacOs M1 doesn't work correctly or in the execution the message
(mach-o file, but is an incompatible architecture (have 'x86_64', need 'arm64e')) appears, then try
ARCHFLAGS="-arch arm64" python setup.py develop
To run a quick sanity check, leave that directory (e.g. by executing cd ~/)
and try to import the library.
python -c "import torchrl"
This should not return any warning or error.
Optional dependencies
The following libraries can be installed depending on the usage one wants to make of torchrl:
# diverse
pip3 install tqdm tensorboard "hydra-core>=1.1" hydra-submitit-launcher
# rendering
pip3 install moviepy
# deepmind control suite
pip3 install dm_control
# gym, atari games
pip3 install "gym[atari]" "gym[accept-rom-license]" pygame
# tests
pip3 install pytest pyyaml pytest-instafail
# tensorboard
pip3 install tensorboard
# wandb
pip3 install wandb
Troubleshooting
If a ModuleNotFoundError: No module named ‘torchrl._torchrl errors occurs,
it means that the C++ extensions were not installed or not found.
One common reason might be that you are trying to import torchrl from within the
git repo location. Indeed the following code snippet should return an error if
torchrl has not been installed in develop mode:
cd ~/path/to/rl/repo
python -c 'from torchrl.envs.libs.gym import GymEnv'
If this is the case, consider executing torchrl from another location.
On MacOs, we recommend installing XCode first. With Apple Silicon M1 chips, make sure you are using the arm64-built python (e.g. here). Running the following lines of code
wget https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py
python collect_env.py
should display
OS: macOS *** (arm64)
and not
OS: macOS **** (x86_64)
Versioning issues can cause error message of the type undefined symbol and such. For these, refer to the versioning issues document for a complete explanation and proposed workarounds.
Examples are coded in a very similar way but the configuration may change from one algorithm to another (e.g. async/sync data collection, hyperparameters, ratio of model updates / frame etc.)
Check the examples markdown directory for more details about handling the various configuration settings.
In the near future, we plan to:
- provide tutorials on how to design new actors or environment wrappers;
- implement IMPALA (as a distributed RL example) and Meta-RL algorithms;
- improve the tests, documentation and nomenclature.
We welcome any contribution, should you want to contribute to these new features or any other, lister or not, in the issues section of this repository.
Internal collaborations to torchrl are welcome! Feel free to fork, submit issues and PRs. You can checkout the detailed contribution guide here. As mentioned above, a list of open contributions can be found in here.
Contributors are recommended to install pre-commit hooks (using pre-commit install). pre-commit will check for linting related issues when the code is commited locally. You can disable th check by appending -n to your commit command: git commit -m <commit message> -n
This library is not officially released yet and is subject to change.
The features are available before an official release so that users and collaborators can get early access and provide feedback. No guarantee of stability, robustness or backward compatibility is provided.
TorchRL is licensed under the MIT License. See LICENSE for details.