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A modular reinforcement learning library

Project description

ModulaRL

ModulaRL Logo
🚧 This library is still under construction. 🚧

Code style: black pytest

ModulaRL is a highly modular and extensible reinforcement learning library built on PyTorch. It aims to provide researchers and developers with a flexible framework for implementing, experimenting with, and extending various RL algorithms.

Features

  • Modular architecture allowing easy component swapping and extension
  • Efficient implementations leveraging PyTorch's capabilities
  • Integration with TorchRL for optimized replay buffers
  • Clear documentation and examples for quick start
  • Designed for both research and practical applications in reinforcement learning

Installation

pip install modularl

Algorithms Implemented

Algorithm Type Paper
SAC (Soft Actor-Critic) Off-policy Haarnoja et al. 2018

Citation

@software{modularl2024,
  author = {zakaria narjis},
  title = {ModulaRL: A Modular Reinforcement Learning Library},
  year = {2024},
  url = {https://github.com/zakaria-narjis/modularl}
}

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