pytorch/rl

Health v2 · maintenance only

Adoption not part of the score

3538 stars · 479 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

TorchRL is a PyTorch-native library of composable primitives for building reinforcement learning systems, including environments, policies, replay buffers, collectors, and loss modules. It scales from local prototypes to vectorized, multiprocess, and distributed training while staying close to the PyTorch programming model.

Use cases

• train reinforcement learning agents in pytorch
• build multi-agent reinforcement learning experiments
• run RL for robotics control with MuJoCo
• implement custom replay buffers and data collectors
• prototype model-based reinforcement learning algorithms
• scale RL training across distributed workers

When to choose

• you want a modular, primitive-first RL library integrated with PyTorch
• you need multi-agent or recurrent RL support
• you want to scale RL research from prototype to distributed training

When to avoid

• you need a one-line prebuilt agent for a standard benchmark
• you work outside the PyTorch ecosystem, e.g. TensorFlow or JAX
• you only need lightweight bandit or simple tabular RL

Facets

library · maturity active

machine-learning reinforcement-learning simulation robotics reinforcement-learning machine-learning robotics artificial-intelligence python cross-platform pytorch torchrl rl multi-agent model-based-rl replay-buffers decision-making gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="pytorch/rl")