A modular reinforcement learning library
Project description
ModulaRL
🚧 This library is still under construction. 🚧
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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