garage

Garage is a toolkit for developing and evaluating reinforcement learning algorithms, with an accompanying library of implementations built using it. The toolkit offers modular tools including composable neural network models, replay buffers, high-performance samplers, an experiment definition interface, reproducibility tools, logging including TensorBoard, checkpointing and resuming, and environment interfaces.

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0 shouts Added Sep 23, 2026 github.com
github.com/rlworkgroup/garage
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Features

  • wide range of modular tools for implementing RL algorithms Source: github.com

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