TorchRL: The Reinforcement Learning and Control library for PyTorch
summary
TorchRL presents a PyTorch-first RL library that is modular, Python-native and production-aware, with a separate open-source TensorDict data carrier that enables dictionary-level tensor ops and stacking. Core dependency is PyTorch only; integrations include Gym, DeepMind Control, Habitat and Jumanji. It ships an efficient distributed replay buffer, vectorized and multi-process envs, async data collectors, and a trainer with checkpointing and logging. Ready-made objectives include SAC, DDPG, PPO and examples; components accept plain tensors or TensorDicts and are optimized for research workflows.
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chapters (6)
- 0:00Introduction to TorchRL
- 1:33Design philosophy
- 3:19Core pillars and modularity
- 5:01TensorDict data structure
- 7:45Usability and flexibility
- 8:55Features and conclusion
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