TorchRL

TorchRL is a PyTorch-native toolkit for reinforcement learning, decision making, robotics, and simulation. It is not a single algorithm implementation or a narrow benchmark suite: it is a collection of composable pieces for building RL systems while keeping the code close to the PyTorch programming model. Recent work has made this especially strong for recurrent RL, MuJoCo-based control, multi-agent training, replay-buffer and collector infrastructure, and reusable loss/value-estimation components.

The library is built around three ideas:

1. Data should have names, structure, batch dimensions, and devices all the way through the training loop.
2. Environments, policies, replay buffers, objectives, and collectors should be independent modules that can be swapped without rewriting the rest of the stack.
3. Research code should scale from a local prototype to vectorized, multiprocess, distributed, compiled, recurrent, multi-agent, model-based, or offline workflows without changing the data model.

That common data model is TensorDict, a dictionary-like tensor container with PyTorch operations, device transfers, shared-memory support, memmaps, lazy views, and nn.Module wrappers.

Getting started | API reference | Tutorials | Knowledge base | Examples | SOTA implementations

What's new in TorchRL 0.15

TorchRL 0.15 introduces powerful new capabilities for robot learning, offline RL, data interoperability, and training at scale.

• Robotics and deployment. Use MuJoCo Menagerie and MicroDuck environments, compose low-level controllers, and export TensorDict policies to ONNX. Menagerie example · Low-level control tutorial
• Model-based and offline RL. TD-MPC2, Flow Q-Learning, and Transitive RL provide components and training implementations for control and offline RL. TD-MPC2 · FQL · Transitive RL
• PyTorch and dataset interoperability. Use buffer.as_dataset() with a PyTorch DataLoader, or buffer.export(...) to exchange trajectory data with LeRobot and Hugging Face Datasets. PyTorch data integration · LeRobot tutorial
• Data collection. Use replay_write_mode="trajectory" to write complete episodes to replay, with new readiness and policy-version controls. Collectors and execution models
• Evaluation and checkpointing. Evaluate across environments, schedule evaluation during training, and resume supported recipes from checkpoints. Evaluation · Checkpointing
• Sequence models and RLHF. Build Transformer-based policies and train reward models from preference data. Transformer example · Reward-model recipe

A quick mental model

TorchRL represents an RL interaction as a TensorDict that moves through a small number of reusable components:

TensorDict
  -> policy module writes actions and log-probs
  -> environment reads actions and writes next observations, rewards, done flags
  -> collector batches trajectories from one or many workers
  -> replay buffer stores, samples, prioritizes, and transforms data
  -> loss module reads named keys and writes differentiable losses
  -> optimizer updates ordinary PyTorch parameters

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