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

TODO

Add new algorithms
Add exploration modules
Add experiment wrapper modules

Installation

pip install modularl

Algorithms Implemented

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

Citation

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

Metadata

Release files for modularl 0.1.5

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for modularl 0.1.5
File	Size	Uploaded
modularl-0.1.5.tar.gz	12.9 kB	Aug 9, 2024	Details

Built distribution (wheel)

Table of built distributions (wheels) for modularl 0.1.5
File	Interpreter	ABI	Platform	Reset
modularl-0.1.5-py3-none-any.whl 19.7 kB Aug 9, 2024	Python 3	none	any	Details

Total release size: 32.6 kB

Release files / modularl-0.1.5.tar.gz

Download URL	modularl-0.1.5.tar.gz
Size	12.9 kB
Tags	Source
SHA-256 checksum How to use checksums	`d0ec8b06b01e4e44685a8af58275eb019a19cf1b140efd53e55e8ba80b67414c`
BLAKE2b-256 checksum How to use checksums	`aab23412f476915fc0a9ddd55697357e68319192a87ab6da0ba28c867f2ac78e`
Upload date	Aug 9, 2024
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/5.1.1 CPython/3.9.19`

Release files / modularl-0.1.5-py3-none-any.whl

Download URL	modularl-0.1.5-py3-none-any.whl
Size	19.7 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`c48385cc8f341e00b43b1f21adf7d26d1696e4a4bbc8211737ab453cdb1bf40e`
BLAKE2b-256 checksum How to use checksums	`b902f36b9b333d49b2e59d6d75df8f9122047337485deebfa44bd352e6a8d419`
Upload date	Aug 9, 2024
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/5.1.1 CPython/3.9.19`