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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.

TorchRL now provides apowerful 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 withenv=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 thecomplete CLI documentation to get started!

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: NewRLvLLMEngine interface standardizing all vLLM backends with simplifiedvLLMUpdaterV2 for seamless weight updates
• 🌐Distributed Data Loading: NewRayDataLoadingPrimer 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!fromtorchrl.modules.llmimportAsyncVLLM,vLLMWrapper# Create distributed vLLM service with load balancingservice=AsyncVLLM.from_pretrained("Qwen/Qwen2.5-7B",num_devices=2,# Tensor parallel across 2 GPUsnum_replicas=4,# 4 replicas for high throughputmax_model_len=4096)# Use with TorchRL's LLM wrapperswrapper=vLLMWrapper(service,input_mode="history")# Simplified weight updatesfromtorchrl.collectors.llmimportvLLMUpdaterV2updater=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.

TorchRL now includes anexperimental 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 injust ~20 lines!

Working Example: Seesota-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]"

importhydrafromtorchrl.trainers.algorithms.configsimport*@hydra.main(config_path="config",config_name="config",version_base="1.1")defmain(cfg):trainer=hydra.utils.instantiate(cfg.trainer)trainer.train()if__name__=="__main__":main()

# Basic usage - train PPO on Pendulum-v1 with default settingspython sota-implementations/ppo_trainer/train.py# Custom configuration with command-line overridespython 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 loggerpython sota-implementations/ppo_trainer/train.py \    env=gym \    training_env.create_env_fn.base_env.env_name=Walker2d-v4 \    logger=tensorboard# See all available optionspython 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.

TorchRL includes a comprehensiveLLM 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: AdvancedHistory 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) andSFT 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 thecomplete documentation andGRPO implementation example to get started!

fromtorchrl.envs.llmimportChatEnvfromtorchrl.modules.llmimportTransformersWrapperfromtorchrl.objectives.llmimportGRPOLossfromtorchrl.collectors.llmimportLLMCollector# Create environment with Python tool executionenv=ChatEnv(tokenizer=tokenizer,system_prompt="You are an assistant that can execute Python code.",batch_size=[1]).append_transform(PythonInterpreter())# Wrap your language modelllm=TransformersWrapper(model=model,tokenizer=tokenizer,input_mode="history")# Set up GRPO trainingloss_fn=GRPOLoss(llm,critic,gamma=0.99)collector=LLMCollector(env,llm,frames_per_batch=100)# Training loopfordataincollector:loss=loss_fn(data)loss.backward()optimizer.step()

• 🐍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

• 🔥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 forcommon environment libraries (e.g., OpenAI Gym) and datasets (D4RL, OpenX...)

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

Check ourGetting Started tutorials for quickly ramp up with the basicfeatures of the library!

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

TorchRL also provides a RL knowledge base to help you debug your code, or simplylearn the basics of RL. Check it outhere.

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

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

• ACEGEN: Reinforcement Learning of Generative Chemical Agentsfor Drug Discovery
• BenchMARL: Benchmarking Multi-Agent Reinforcement Learning
• BricksRL: A Platform for Democratizing Robotics and Reinforcement LearningResearch 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

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

importtorchfromtensordict.nnimportTensorDictModulefromtensordict.nn.distributionsimportNormalParamExtractorfromtorchimportnnfromtorchrl.collectorsimportSyncDataCollectorfromtorchrl.data.replay_buffersimportTensorDictReplayBuffer, \LazyTensorStorage,SamplerWithoutReplacementfromtorchrl.envs.libs.gymimportGymEnvfromtorchrl.modulesimportProbabilisticActor,ValueOperator,TanhNormalfromtorchrl.objectivesimportClipPPOLossfromtorchrl.objectives.valueimportGAEenv=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)fordataincollector:# collect dataforepochinrange(10):adv_fn(data)# compute advantagebuffer.extend(data)forsampleinbuffer:# consume dataloss_vals=loss_fn(sample)loss_val=sum(valueforkey,valueinloss_vals.items()ifkey.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 theenvironment APIrelies on tensordict to carry data from one function to another during a rolloutexecution:

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

- 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 primitivesto be easily recycled across settings.

- 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()

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):

# stack and cattensordict=torch.stack(list_of_tensordicts,0)tensordict=torch.cat(list_of_tensordicts,0)# reshapetensordict=tensordict.view(-1)tensordict=tensordict.permute(0,2,1)tensordict=tensordict.unsqueeze(-1)tensordict=tensordict.squeeze(-1)# indexingtensordict=tensordict[:2]tensordict[:,2]=sub_tensordict# device and memory locationtensordict.cuda()tensordict.to("cuda:1")tensordict.share_memory_()

TensorDict comes with a dedicatedtensordict.nnmodule that contains everything you might need to write your model with it.And it isfunctorch andtorch.compile compatible!

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"]

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)asserttransformer.in_keys== ["src","src_mask","tgt"]asserttransformer.out_keys== ["memory","output"]

transformer.select_subsequence(out_keys=["memory"])# returns the encodertransformer.select_subsequence(in_keys=["tgt","memory"])# returns the decoder

CheckTensorDict tutorials tolearn more!

• A commoninterface for environmentswhich supports common libraries (OpenAI gym, deepmind control lab, etc.)⁽¹⁾ and state-less execution(e.g. Model-based environments).Thebatched environments containers allow parallel execution⁽²⁾.A common PyTorch-first class oftensor-specification class is also provided.TorchRL's environments API is simple but stringent and specific. Check thedocumentationandtutorial to learn more!

  Code

  env_make=lambda:GymEnv("Pendulum-v1",from_pixels=True)env_parallel=ParallelEnv(4,env_make)# creates 4 envs in paralleltensordict=env_parallel.rollout(max_steps=20,policy=None)# random rollout (no policy given)asserttensordict.shape== [4,20]# 4 envs, 20 steps rolloutenv_parallel.action_spec.is_in(tensordict["action"])# spec check returns True

• multiprocess and distributeddata collectors⁽²⁾that work synchronously or asynchronously.Through the use of TensorDict, TorchRL's training loops are made very similarto regular training loops in supervisedlearning (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,    ...)fori,tensordict_datainenumerate(collector):loss=loss_module(tensordict_data)loss.backward()optim.step()optim.zero_grad()collector.update_policy_weights_()

  Check ourdistributed collector examples tolearn more about ultra-fast data collection with TorchRL.

• efficient⁽²⁾ and generic⁽¹⁾replay buffers with modularized storage:

  Code

  storage=LazyMemmapStorage(# memory-mapped (physical) storagecfg.buffer_size,scratch_dir="/tmp/")buffer=TensorDictPrioritizedReplayBuffer(alpha=0.7,beta=0.5,collate_fn=lambdax:x,pin_memory=device!=torch.device("cpu"),prefetch=10,# multi-threaded samplingstorage=storage)

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

  Code

  fromtorchrl.data.replay_buffersimportSamplerWithoutReplacementfromtorchrl.data.datasets.d4rlimportD4RLExperienceReplaydata=D4RLExperienceReplay("maze2d-open-v0",split_trajs=True,batch_size=128,sampler=SamplerWithoutReplacement(drop_last=True),)forsampleindata:# or alternatively sample = data.sample()fun(sample)

• cross-libraryenvironment 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 parallelenv=TransformedEnv(env_base,Compose(ToTensorImage(),ObservationNorm(loc=0.5,scale=1.0)),# executes the transforms once and on device)tensordict=env.reset()asserttensordict.device==torch.device("cuda:0")

  Other transforms include: reward scaling (RewardScaling), shape operations (concatenation of tensors, unsqueezing etc.), concatenation ofsuccessive 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 iteasy 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 listparent_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⁾;

• variousarchitectures and models (e.g.actor-critic)⁽¹⁾:

  Code

  # create an nn.Modulecommon_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 outputcommon_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 spacepolicy_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 itpolicy_module=SafeProbabilisticTensorDictSequential(# stochastic policypolicy_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 moduleactor_value=ActorValueOperator(common_module,policy_module,value_module)# standalone policy from thisstandalone_policy=actor_value.get_policy_operator()

• explorationwrappers andmodules to easily swap between exploration and exploitation⁽¹⁾:

  Code

  policy_explore=EGreedyWrapper(policy)withset_exploration_type(ExplorationType.RANDOM):tensordict=policy_explore(tensordict)# will use eps-greedywithset_exploration_type(ExplorationType.DETERMINISTIC):tensordict=policy_explore(tensordict)# will not use eps-greedy

• A series of efficientloss modulesand highly vectorizedfunctional return and advantagecomputation.

  Code

  Loss modules

  fromtorchrl.objectivesimportDQNLossloss_module=DQNLoss(value_network=value_network,gamma=0.99)tensordict=replay_buffer.sample(batch_size)loss=loss_module(tensordict)

  Advantage computation

  fromtorchrl.objectives.value.functionalimportvec_td_lambda_return_estimateadvantage=vec_td_lambda_return_estimate(gamma,lmbda,next_state_value,reward,done,terminated)

• a generictrainer class⁽¹⁾ thatexecutes the aforementioned training loop. Through a hooking mechanism,it also supports any logging or data transformation operation at any giventime.

• variousrecipes to build models thatcorrespond 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

  fromtorchrl.envs.llmimportChatEnvfromtorchrl.modules.llmimportTransformersWrapperfromtorchrl.envs.llm.transformsimportPythonInterpreter# Create environment with tool executionenv=ChatEnv(tokenizer=tokenizer,system_prompt="You can execute Python code.",batch_size=[1]).append_transform(PythonInterpreter())# Wrap language model for trainingllm=TransformersWrapper(model=model,tokenizer=tokenizer,input_mode="history")# Multi-turn conversation with tool useobs=env.reset(TensorDict({"query":"Calculate 2+2"},batch_size=[1]))llm_output=llm(obs)# Generates responseobs=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 ourcall for contributions and ourcontribution page.

A series ofState-of-the-Art implementations are provided with an illustrative purpose:

** The number indicates expected speed-up compared to eager mode when executed on CPU. Numbers may vary depending onarchitecture 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 theexamples directory for more detailsabout handling the various configuration settings.

We also providetutorials and demos that give a sense ofwhat the library can do.

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}}

python -m venv torchrlsource 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=3.10conda activate torchrl

Depending on the use of torchrl that you want to make, you may want toinstall the latest (nightly) PyTorch release or the latest stable version of PyTorch.Seehere for a detailed list of commands,includingpip3 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

You can install thelatest 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 cloningpip3 install git+https://github.com/pytorch/rl@v0.8.1# Clone the library and build it locallygit clone https://github.com/pytorch/tensordictgit clone https://github.com/pytorch/rlpip install -e tensordictpip install -e rl

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

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

Note that tensordict local build requirescmake to be installed viahomebrew (MacOS) or another package managersuch asapt,apt-get,conda oryum but NOTpip, as well aspip install "pybind11[global]".

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

pip install buildpython -m build --wheel

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

pip install torchrl<name>.whl

Thenightly 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 notdirectly require a newer version of pytorch to be installed. Indirectly though, tensordict still requires the latestPyTorch 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 alsobe limited with older versions of pytorch. It is recommended to use the latest TorchRL with the latest PyTorch versionunless there is a strong reason not to do so.

Optional dependencies

The following libraries can be installed depending on the usage one wants tomake of torchrl:

# diversepip3 install tqdm tensorboard "hydra-core>=1.1" hydra-submitit-launcher# renderingpip3 install "moviepy<2.0.0"# deepmind control suitepip3 install dm_control# gym, atari gamespip3 install "gym[atari]" "gym[accept-rom-license]" pygame# testspip3 install pytest pyyaml pytest-instafail# tensorboardpip3 install tensorboard# wandbpip3 install wandb

Versioning issues can cause error message of the typeundefined symboland such. For these, refer to theversioning issues documentfor a complete explanation and proposed workarounds.

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 onthePyTorch forum.

Internal collaborations to torchrl are welcome! Feel free to fork, submit issues and PRs.You can checkout the detailed contribution guidehere.As mentioned above, a list of open contributions can be found inhere.

Contributors are recommended to installpre-commit hooks (usingpre-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

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

TorchRL is licensed under the MIT License. SeeLICENSE for details.