torchrl 0.11.1

A modular, primitive-first, python-first PyTorch library for Reinforcement Learning

TorchRL

What's New | LLM API | Getting Started | Documentation | TensorDict | Features | Examples, tutorials and demos | Citation | Installation | Asking a question | Contributing

TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch.

🚀 What's New

🚀 Command-Line Training Interface - Train RL Agents Without Writing Code! (Experimental)

TorchRL now provides a powerful command-line interface that lets you train state-of-the-art RL agents with simple bash commands! No Python scripting required - just run training with customizable parameters:

• 🎯 One-Command Training: python sota-implementations/ppo_trainer/train.py
• ⚙️ Full Customization: Override any parameter via command line: trainer.total_frames=2000000 optimizer.lr=0.0003
• 🌍 Multi-Environment Support: Switch between Gym, Brax, DM Control, and more with env=gym training_env.create_env_fn.base_env.env_name=HalfCheetah-v4
• 📊 Built-in Logging: TensorBoard, Weights & Biases, CSV logging out of the box
• 🔧 Hydra-Powered: Leverages Hydra's powerful configuration system for maximum flexibility
• 🏃‍♂️ Production Ready: Same robust training pipeline as our SOTA implementations

Perfect for: Researchers, practitioners, and anyone who wants to train RL agents without diving into implementation details.

⚠️ Note: This is an experimental feature. The API may change in future versions. We welcome feedback and contributions to help improve this implementation!

📋 Prerequisites: The training interface requires Hydra for configuration management. Install with:

pip install "torchrl[utils]"
# or manually:
pip install hydra-core omegaconf

Check out the complete CLI documentation to get started!

🚀 vLLM Revamp - Major Enhancement to LLM Infrastructure (v0.10)

This release introduces a comprehensive revamp of TorchRL's vLLM integration, delivering significant improvements in performance, scalability, and usability for large language model inference and training workflows:

• 🔥 AsyncVLLM Service: Production-ready distributed vLLM inference with multi-replica scaling and automatic Ray actor management
• ⚖️ Multiple Load Balancing Strategies: Routing strategies including prefix-aware, request-based, and KV-cache load balancing for optimal performance
• 🏗️ Unified vLLM Architecture: New RLvLLMEngine interface standardizing all vLLM backends with simplified vLLMUpdaterV2 for seamless weight updates
• 🌐 Distributed Data Loading: New RayDataLoadingPrimer for shared, distributed data loading across multiple environments
• 📈 Enhanced Performance: Native vLLM batching, concurrent request processing, and optimized resource allocation via Ray placement groups

# Simple AsyncVLLM usage - production ready!
from torchrl.modules.llm import AsyncVLLM, vLLMWrapper

# Create distributed vLLM service with load balancing
service = AsyncVLLM.from_pretrained(
    "Qwen/Qwen2.5-7B",
    num_devices=2,      # Tensor parallel across 2 GPUs
    num_replicas=4,     # 4 replicas for high throughput
    max_model_len=4096
)

# Use with TorchRL's LLM wrappers
wrapper = vLLMWrapper(service, input_mode="history")

# Simplified weight updates
from torchrl.collectors.llm import vLLMUpdaterV2
updater = vLLMUpdaterV2(service)  # Auto-configures from engine

This revamp positions TorchRL as the leading platform for scalable LLM inference and training, providing production-ready tools for both research and deployment scenarios.

🧪 PPOTrainer (Experimental) - High-Level Training Interface

TorchRL now includes an experimental PPOTrainer that provides a complete, configurable PPO training solution! This prototype feature combines TorchRL's modular components into a cohesive training system with sensible defaults:

• 🎯 Complete Training Pipeline: Handles environment setup, data collection, loss computation, and optimization automatically
• ⚙️ Extensive Configuration: Comprehensive Hydra-based config system for easy experimentation and hyperparameter tuning
• 📊 Built-in Logging: Automatic tracking of rewards, actions, episode completion rates, and training statistics
• 🔧 Modular Design: Built on existing TorchRL components (collectors, losses, replay buffers) for maximum flexibility
• 📝 Minimal Code: Complete SOTA implementation in just ~20 lines!

Working Example: See sota-implementations/ppo_trainer/ for a complete, working PPO implementation that trains on Pendulum-v1 with full Hydra configuration support.

Prerequisites: Requires Hydra for configuration management: pip install "torchrl[utils]"

Complete Training Script (sota-implementations/ppo_trainer/train.py)

import hydra
from torchrl.trainers.algorithms.configs import *

@hydra.main(config_path="config", config_name="config", version_base="1.1")
def main(cfg):
    trainer = hydra.utils.instantiate(cfg.trainer)
    trainer.train()

if __name__ == "__main__":
    main()

Complete PPO training in ~20 lines with full configurability.

API Usage Examples

# Basic usage - train PPO on Pendulum-v1 with default settings
python sota-implementations/ppo_trainer/train.py

# Custom configuration with command-line overrides
python sota-implementations/ppo_trainer/train.py \
    trainer.total_frames=2000000 \
    training_env.create_env_fn.base_env.env_name=HalfCheetah-v4 \
    networks.policy_network.num_cells=[256,256] \
    optimizer.lr=0.0003

# Use different environment and logger
python sota-implementations/ppo_trainer/train.py \
    env=gym \
    training_env.create_env_fn.base_env.env_name=Walker2d-v4 \
    logger=tensorboard

# See all available options
python sota-implementations/ppo_trainer/train.py --help

Future Plans: Additional algorithm trainers (SAC, TD3, DQN) and full integration of all TorchRL components within the configuration system are planned for upcoming releases.

LLM API - Complete Framework for Language Model Fine-tuning

TorchRL includes a comprehensive LLM API for post-training and fine-tuning of language models! This framework provides everything you need for RLHF, supervised fine-tuning, and tool-augmented training:

• 🤖 Unified LLM Wrappers: Seamless integration with Hugging Face models and vLLM inference engines
• 💬 Conversation Management: Advanced History class for multi-turn dialogue with automatic chat template detection
• 🛠️ Tool Integration: Built-in support for Python code execution, function calling, and custom tool transforms
• 🎯 Specialized Objectives: GRPO (Group Relative Policy Optimization) and SFT loss functions optimized for language models
• ⚡ High-Performance Collectors: Async data collection with distributed training support
• 🔄 Flexible Environments: Transform-based architecture for reward computation, data loading, and conversation augmentation

The LLM API follows TorchRL's modular design principles, allowing you to mix and match components for your specific use case. Check out the complete documentation and GRPO implementation example to get started!

Quick LLM API Example

from torchrl.envs.llm import ChatEnv
from torchrl.modules.llm import TransformersWrapper
from torchrl.objectives.llm import GRPOLoss
from torchrl.collectors.llm import LLMCollector

# Create environment with Python tool execution
env = ChatEnv(
    tokenizer=tokenizer,
    system_prompt="You are an assistant that can execute Python code.",
    batch_size=[1]
).append_transform(PythonInterpreter())

# Wrap your language model
llm = TransformersWrapper(
    model=model,
    tokenizer=tokenizer,
    input_mode="history"
)

# Set up GRPO training
loss_fn = GRPOLoss(llm, critic, gamma=0.99)
collector = LLMCollector(env, llm, frames_per_batch=100)

# Training loop
for data in collector:
    loss = loss_fn(data)
    loss.backward()
    optimizer.step()

Key features

• 🐍 Python-first: Designed with Python as the primary language for ease of use and flexibility
• ⏱️ Efficient: Optimized for performance to support demanding RL research applications
• 🧮 Modular, customizable, extensible: Highly modular architecture allows for easy swapping, transformation, or creation of new components
• 📚 Documented: Thorough documentation ensures that users can quickly understand and utilize the library
• ✅ Tested: Rigorously tested to ensure reliability and stability
• ⚙️ Reusable functionals: Provides a set of highly reusable functions for cost functions, returns, and data processing

Design Principles

• 🔥 Aligns with PyTorch ecosystem: Follows the structure and conventions of popular PyTorch libraries (e.g., dataset pillar, transforms, models, data utilities)
• ➖ Minimal dependencies: Only requires Python standard library, NumPy, and PyTorch; optional dependencies for common environment libraries (e.g., OpenAI Gym) and datasets (D4RL, OpenX...)

Read the full paper for a more curated description of the library.

Getting started

Check our Getting Started tutorials for quickly ramp up with the basic features of the library!

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:

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

Spotlight publications

TorchRL being domain-agnostic, you can use it across many different fields. Here are a few examples:

• ACEGEN: Reinforcement Learning of Generative Chemical Agents for Drug Discovery
• BenchMARL: Benchmarking Multi-Agent Reinforcement Learning
• BricksRL: A Platform for Democratizing Robotics and Reinforcement Learning Research and Education with LEGO
• OmniDrones: An Efficient and Flexible Platform for Reinforcement Learning in Drone Control
• RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
• Robohive: A unified framework for robot learning

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={"low": -1.0, "high": 1.0},
  return_log_prob=True
  )
buffer = TensorDictReplayBuffer(
  storage=LazyTensorStorage(1000),
  sampler=SamplerWithoutReplacement(),
  batch_size=50,
  )
collector = SyncDataCollector(
  env,
  actor,
  frames_per_batch=1000,
  total_frames=1_000_000,
)
loss_fn = ClipPPOLoss(actor, critic)
adv_fn = GAE(value_network=critic, average_gae=True, gamma=0.99, lmbda=0.95)
optim = torch.optim.Adam(loss_fn.parameters(), lr=2e-4)

for data in collector:  # collect data
  for epoch in range(10):
      adv_fn(data)  # compute advantage
      buffer.extend(data)
      for sample in buffer:  # consume data
          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.), concatenation 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 SafeProbabilisticTensorDictSequential to combine the SafeModule with a
  # SafeProbabilisticModule, indicating how to build the
  # torch.distribution.Distribution object and what to do with it
  policy_module = SafeProbabilisticTensorDictSequential(  # 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_type(ExplorationType.RANDOM):
      tensordict = policy_explore(tensordict)  # will use eps-greedy
  with set_exploration_type(ExplorationType.DETERMINISTIC):
      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, terminated)
  

• 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.

• LLM API: Complete framework for language model fine-tuning with unified wrappers for Hugging Face and vLLM backends, conversation management with automatic chat template detection, tool integration (Python execution, function calling), specialized objectives (GRPO, SFT), and high-performance async collectors. Perfect for RLHF, supervised fine-tuning, and tool-augmented training scenarios.

  Code

  from torchrl.envs.llm import ChatEnv
  from torchrl.modules.llm import TransformersWrapper
  from torchrl.envs.llm.transforms import PythonInterpreter
  
  # Create environment with tool execution
  env = ChatEnv(
      tokenizer=tokenizer,
      system_prompt="You can execute Python code.",
      batch_size=[1]
  ).append_transform(PythonInterpreter())
  
  # Wrap language model for training
  llm = TransformersWrapper(
      model=model,
      tokenizer=tokenizer,
      input_mode="history"
  )
  
  # Multi-turn conversation with tool use
  obs = env.reset(TensorDict({"query": "Calculate 2+2"}, batch_size=[1]))
  llm_output = llm(obs)  # Generates response
  obs = env.step(llm_output)  # Environment processes response
  

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 State-of-the-Art implementations are provided with an illustrative purpose:

Algorithm	Compile Support**	Tensordict-free API	Modular Losses	Continuous and Discrete
DQN	1.9x	+	NA	+ (through ActionDiscretizer transform)
DDPG	1.87x	+	+	- (continuous only)
IQL	3.22x	+	+	+
CQL	2.68x	+	+	+
TD3	2.27x	+	+	- (continuous only)
TD3+BC	untested	+	+	- (continuous only)
A2C	2.67x	+	-	+
PPO	2.42x	+	-	+
SAC	2.62x	+	-	+
REDQ	2.28x	+	-	- (continuous only)
Dreamer v1	untested	+	+ (different classes)	- (continuous only)
Decision Transformers	untested	+	NA	- (continuous only)
CrossQ	untested	+	+	- (continuous only)
Gail	untested	+	NA	+
Impala	untested	+	-	+
IQL (MARL)	untested	+	+	+
DDPG (MARL)	untested	+	+	- (continuous only)
PPO (MARL)	untested	+	-	+
QMIX-VDN (MARL)	untested	+	NA	+
SAC (MARL)	untested	+	-	+
RLHF	NA	+	NA	NA
LLM API (GRPO)	NA	+	+	NA

** The number indicates expected speed-up compared to eager mode when executed on CPU. Numbers may vary depending on architecture and device.

and many more to come!

Code examples displaying toy code snippets and training scripts are also available

• LLM API & GRPO - Complete language model fine-tuning pipeline
• RLHF
• Memory-mapped replay buffers

Check the examples 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.

Citation

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

@misc{bou2023torchrl,
      title={TorchRL: A data-driven decision-making library for PyTorch}, 
      author={Albert Bou and Matteo Bettini and Sebastian Dittert and Vikash Kumar and Shagun Sodhani and Xiaomeng Yang and Gianni De Fabritiis and Vincent Moens},
      year={2023},
      eprint={2306.00577},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Installation

Create a new virtual environment:

python -m venv torchrl
source torchrl/bin/activate  # On Windows use: venv\Scripts\activate

Or create a conda environment where the packages will be installed.

conda create --name torchrl python=3.10
conda activate torchrl

Install dependencies:

PyTorch

Depending on the use of torchrl 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 other special installation instructions.

TorchRL offers a few pre-defined dependencies such as "torchrl[tests]", "torchrl[atari]", "torchrl[utils]" etc.

For the experimental training interface and configuration system, install:

pip3 install "torchrl[utils]"  # Includes hydra-core and other utilities

Torchrl

You can install the latest stable release by using

pip3 install torchrl

This should work on linux (including AArch64 machines), Windows 10 and OsX (Metal chips only). On certain Windows machines (Windows 11), one should build the library locally. This can be done in two ways:

# Install and build locally v0.8.1 of the library without cloning
pip3 install git+https://github.com/pytorch/rl@v0.8.1
# Clone the library and build it locally
git clone https://github.com/pytorch/tensordict
git clone https://github.com/pytorch/rl
pip install -e tensordict
pip install -e rl

If you use uv (instead of pip) and you have already installed a specific PyTorch build (e.g. nightly), make sure uv doesn't re-resolve dependencies (which can downgrade PyTorch). Use --no-deps for the local installs:

uv pip install --no-deps -e tensordict
uv pip install --no-deps -e rl

Note that tensordict local build requires cmake to be installed via homebrew (MacOS) or another package manager such as apt, apt-get, conda or yum but NOT pip, as well as pip install "pybind11[global]".

One can also build the wheels to distribute to co-workers using

pip install build
python -m build --wheel

Your wheels will be stored there ./dist/torchrl<name>.whl and installable via

pip install torchrl<name>.whl

The nightly build can be installed via

pip3 install tensordict-nightly torchrl-nightly

which we currently only ship for Linux machines. Importantly, the nightly builds require the nightly builds of PyTorch too. Also, a local build of torchrl with the nightly build of tensordict may fail - install both nightlies or both local builds but do not mix them.

Disclaimer: As of today, TorchRL requires Python 3.10+ and is roughly compatible with any pytorch version >= 2.1. Installing it will not directly require a newer version of pytorch to be installed. Indirectly though, tensordict still requires the latest PyTorch to be installed and we are working hard to loosen that requirement. The C++ binaries of TorchRL (mainly for prioritized replay buffers) will only work with PyTorch 2.7.0 and above. Some features (e.g., working with nested jagged tensors) may also be limited with older versions of pytorch. It is recommended to use the latest TorchRL with the latest PyTorch version unless there is a strong reason not to do so.

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<2.0.0"

# 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

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.

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 committed 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.