🚧 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.
- 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
- Add new algorithms
- Add exploration modules
- Add experiment wrapper modules
pip install modularl| Algorithm | Type | Paper | Continuous Action | Discrete Action |
|---|---|---|---|---|
| SAC (Soft Actor-Critic) | Off-policy | Haarnoja et al. 2018 | ✅ | Not implemented YET |
| TD3 (Twin Delayed DDPG) | Off-policy | Fujimoto et al. 2018 | ✅ | Not implemented YET |
| DDPG (Deep Deterministic Policy Gradient) | Off-policy | Lillicrap et al. 2015 | ✅ | Not implemented YET |
@software{modularl2024,
author = {zakaria narjis},
title = {ModulaRL: A Modular Reinforcement Learning Library},
year = {2024},
url = {https://github.com/zakaria-narjis/modularl}
}