/XuanJing

XuanJing is a benchmark library of decision algorithms for reinforcement learning, imitation learning, multi-agent learning and planning algorithms.

Primary LanguagePython

XuanJing

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XuanJing is a benchmark library of decision algorithms for reinforcement learning, imitation learning, multi-agent learning and planning algorithms.

In both supervised learning and reinforcement learning, the algorithm consists of two main components. : the data and the update formula. XuanJing abstracts these two parts, so that it is possible to train reinforcement learning algorithms in the same way as supervised learning.

Status

WIP. Not released yet.

Table of Contents

FileFramework

Env is in responsible for parallelizing and wrapping the environment. The task of interacting with the environment falls to the actor. The data produced during the interaction between the actor and the environment is stored in the buffer(if needed.). When an actor interacts with an environment, learner is in charge of managing the data and algorithms. enhancement is used to enhance the data in the buffer. Model parameters are updated by the learner using data and algorithms. utils are a class of useful functions.

Install

TODO

Usage

TODO

Support

Supported algorithms are as following:

model free reinforcement learning

model based reinforcement learning

Imitation Learning

planning algorithms

evolution strategies

Example Readmes

To see how the specification has been applied, see the example-readmes.

Contributors

This project exists thanks to all the people who contribute.

Made with contributors-img.

License

MIT © tinyzqh

Citation

If you find XuanJing useful, please cite it in your publications.

@software{XuanJing,
  author = {Zhiqiang He},
  title = {XuanJing},
  year = {2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/tinyzqh/XuanJing}},
}