torchrl 0.1.0

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TorchRL

Documentation | TensorDict | Features | Examples, tutorials and demos | Installation | Asking a question | Citation | 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.

Documentation and knowledge base

The TorchRL documentation can be found here. It contains tutorials and the API reference.

TorchRL also provides a RL knowledge base to help you debug your code, or simply learn the basics of RL. Check it out here.

We have some introductory videos for you to get to know the library better, check them out:

• TorchRL intro at PyTorch day 2022
• PyTorch 2.0 Q&A: TorchRL

Writing simplified and portable RL codebase with TensorDict

RL algorithms are very heterogeneous, and it can be hard to recycle a codebase across settings (e.g. from online to offline, from state-based to pixel-based learning). TorchRL solves this problem through TensorDict, a convenient data structure⁽¹⁾ that can be used to streamline one's RL codebase. With this tool, one can write a complete PPO training script in less than 100 lines of code!

Code

import torch
from tensordict.nn import TensorDictModule
from tensordict.nn.distributions import NormalParamExtractor
from torch import nn

from torchrl.collectors import SyncDataCollector
from torchrl.data.replay_buffers import TensorDictReplayBuffer, \
    LazyTensorStorage, SamplerWithoutReplacement
from torchrl.envs.libs.gym import GymEnv
from torchrl.modules import ProbabilisticActor, ValueOperator, TanhNormal
from torchrl.objectives import ClipPPOLoss
from torchrl.objectives.value import GAE

env = GymEnv("Pendulum-v1")
model = TensorDictModule(
    nn.Sequential(
        nn.Linear(3, 128), nn.Tanh(),
        nn.Linear(128, 128), nn.Tanh(),
        nn.Linear(128, 128), nn.Tanh(),
        nn.Linear(128, 2),
        NormalParamExtractor()
    ),
    in_keys=["observation"],
    out_keys=["loc", "scale"]
)
critic = ValueOperator(
    nn.Sequential(
        nn.Linear(3, 128), nn.Tanh(),
        nn.Linear(128, 128), nn.Tanh(),
        nn.Linear(128, 128), nn.Tanh(),
        nn.Linear(128, 1),
    ),
    in_keys=["observation"],
)
actor = ProbabilisticActor(
    model,
    in_keys=["loc", "scale"],
    distribution_class=TanhNormal,
    distribution_kwargs={"min": -1.0, "max": 1.0},
    return_log_prob=True
    )
buffer = TensorDictReplayBuffer(
    LazyTensorStorage(1000),
    SamplerWithoutReplacement()
    )
collector = SyncDataCollector(
    env,
    actor,
    frames_per_batch=1000,
    total_frames=1_000_000
    )
loss_fn = ClipPPOLoss(actor, critic, gamma=0.99)
optim = torch.optim.Adam(loss_fn.parameters(), lr=2e-4)
adv_fn = GAE(value_network=critic, gamma=0.99, lmbda=0.95, average_gae=True)
for data in collector:  # collect data
    for epoch in range(10):
        adv_fn(data)  # compute advantage
        buffer.extend(data.view(-1))
        for i in range(20):  # consume data
            sample = buffer.sample(50)  # mini-batch
            loss_vals = loss_fn(sample)
            loss_val = sum(
                value for key, value in loss_vals.items() if
                key.startswith("loss")
                )
            loss_val.backward()
            optim.step()
            optim.zero_grad()
    print(f"avg reward: {data['next', 'reward'].mean().item(): 4.4f}")

Here is an example of how the environment API relies on tensordict to carry data from one function to another during a rollout execution:

TensorDict makes it easy to re-use pieces of code across environments, models and algorithms.

Code

For instance, here's how to code a rollout in TorchRL:

- 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 operations

Using this, TorchRL abstracts away the input / output signatures of the modules, env, collectors, replay buffers and losses of the library, allowing all primitives to be easily recycled across settings.

Code

Here's another example of an off-policy training loop in TorchRL (assuming that a data collector, a replay buffer, a loss and an optimizer have been instantiated):

- 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()

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_()

TensorDict comes with a dedicated tensordict.nn module that contains everything you might need to write your model with it. And it is functorch and torch.compile 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 TensorDictSequential 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 = TensorDictSequential(encoder_module, in_keys=["src", "src_mask"], out_keys=["memory"])
decoder_module = TransformerDecoder(...)
decoder = TensorDictModule(decoder_module, in_keys=["tgt", "memory"], out_keys=["output"])
transformer = TensorDictSequential(encoder, decoder)
assert transformer.in_keys == ["src", "src_mask", "tgt"]
assert transformer.out_keys == ["memory", "output"]

TensorDictSequential 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 decoder

Check TensorDict tutorials to learn more!

Features

• 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. TorchRL's environments API is simple but stringent and specific. Check the documentation and tutorial to learn more!

  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 and distributed 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_()
  

  Check our distributed collector examples to learn more about ultra-fast data collection with TorchRL.

• 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
  )
  

  Replay buffers are also offered as wrappers around common datasets for offline RL:

  Code

  from torchrl.data.replay_buffers import SamplerWithoutReplacement
  from torchrl.data.datasets.d4rl import D4RLExperienceReplay
  data = D4RLExperienceReplay(
      "maze2d-open-v0",
      split_trajs=True,
      batch_size=128,
      sampler=SamplerWithoutReplacement(drop_last=True),
  )
  for sample in data:  # or alternatively sample = data.sample()
      fun(sample)
  

• 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

  Loss modules

  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)
  

  Advantage computation

  from torchrl.objectives.value.functional import vec_td_lambda_return_estimate
  advantage = vec_td_lambda_return_estimate(gamma, lmbda, next_state_value, reward, done)
  

• 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.

• 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.

Examples, tutorials and demos

A series of examples are provided with an illustrative purpose:

• DQN and Rainbow
• DDPG
• IQL
• TD3
• A2C
• PPO
• SAC
• REDQ
• Dreamer

and many more to come!

Check the examples markdown directory for more details about handling the various configuration settings.

We also provide tutorials and demos that give a sense of what the library can do.

Installation

Create a conda environment where the packages will be installed.

conda create --name torch_rl python=3.9
conda activate torch_rl

PyTorch

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 detailed list of commands, including pip3 or windows/OSX compatible installation commands.

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.

Asking a question

If you spot a bug in the library, please raise an issue in this repo.

If you have a more generic question regarding RL in PyTorch, post it on the PyTorch forum.

Citation

If you're using TorchRL, please refer to this BibTeX entry to cite this work:

@software{TorchRL,
  author = {Moens, Vincent},
  title = {{TorchRL: an open-source Reinforcement Learning (RL) library for PyTorch}},
  url = {https://github.com/pytorch/rl},
  version = {0.1.0},
  year = {2023}
}

Contributing

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

Disclaimer

This library is released as a PyTorch beta feature. BC-breaking changes are likely to happen but they will be introduced with a deprecation warranty after a few release cycles.

License

TorchRL is licensed under the MIT License. See LICENSE for details.