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TorchRL provides highly modular and extensible approach to experimenting with Reinforcement Learning. It allows for a registry based approach to running experiments, allows easy checkpointing, and updating hyper parameter sets. All this is accessible via a programmatic interface as well as a friendly CLI.


  • Modularity in the RL pipeline
  • Clean implementations of fundamental ideas
  • Fast Experimentation
  • Scalability
  • Low bar and High ceiling


pip install torchrl

Install from source for the latest changes that have not been published to PyPI.

pip install

This installs the torchrl package and the torchrl CLI.

What’s next?

Read the Getting Started Guide or see some ready-to-run Experiments.