Skip to main content

Custom OpenAI gym Xiangqi environment

Project description

Gym Xiangqi

CI Test Suite flake8 lint radon analysis Documentation Status PyPI version

Gym Xiangqi is a reinforcement learning environment of Xiangqi, Chinese Chess, game. The project is built on top of a popular reinforcement learning framework called OpenAI Gym.

Welcome Demo

Getting Started

Installing and using Gym Xiangqi is easy. If you are unfamiliar with Xiangqi, the Chinese Chess, we encourage you to read our Wiki page for a starter. We also welcome you to checkout our documentation page, but if you have experiences working with other OpenAI Gym environments you will be already off to a good start.

Prerequisites

In order to use Gym Xiangqi environment for your reinforcement learning project, you need to have,

  • A machine that supports video and audio outputs. For example, a headless server will not be a great choice here.
  • Python 3.6 or above

User Installation

Install Gym Xiangqi on your Python environment using pip

pip install gym-xiangqi

Test your installation by running

agent-v-agent

Development Installation

First, clone the repository

git clone https://github.com/tanliyon/gym-xiangqi.git

or download the latest release and extract the files

Enter the repository

cd gym-xiangqi

Install the core dependencies

pip install -e .

Install the dependencies for development

pip install -r requirements.txt

Software Handbook

Read through the software handbook for comprehensive explanation and development guide for the repository.

Built With

  • [OpenAI Gym] - Used for developing the reinforcement learning environment
  • [PyGame] - Used for rendering the game

Contributing

Please checkout our contribution guidelines in CONTRIBUTING.md and our software handbook.

Versioning

We use Semantic Versioning for versioning. For the versions available, see the tags on this repository.

Authors

License

This project is licensed under the GNU Lesser General Public License v3.0 - see the LICENSE.md file for details.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

gym_xiangqi-0.5.0.tar.gz (39.4 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

gym_xiangqi-0.5.0-py3-none-any.whl (21.2 MB view details)

Uploaded Python 3

File details

Details for the file gym_xiangqi-0.5.0.tar.gz.

File metadata

  • Download URL: gym_xiangqi-0.5.0.tar.gz
  • Upload date:
  • Size: 39.4 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.5

File hashes

Hashes for gym_xiangqi-0.5.0.tar.gz
Algorithm Hash digest
SHA256 15198d13b4712fd3944b632a0d35b21df1e8da557a3139ae1e967d10c2e7121d
MD5 0ebc849a2b589df112f5987af0a00b45
BLAKE2b-256 d5b55ed259f6df51c8bd6f8cc572cb5941eb668bb8ca6937f1606138a12027c0

See more details on using hashes here.

File details

Details for the file gym_xiangqi-0.5.0-py3-none-any.whl.

File metadata

  • Download URL: gym_xiangqi-0.5.0-py3-none-any.whl
  • Upload date:
  • Size: 21.2 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.5

File hashes

Hashes for gym_xiangqi-0.5.0-py3-none-any.whl
Algorithm Hash digest
SHA256 003591e0a8fe32a987f9e5a440c88c77e599c35e2f233eb7e95d44754d5eedc5
MD5 2e17536a774a110c42bca0c41bec1846
BLAKE2b-256 a580f1a082c8a4086341a832059d4790646c69d2e9b84b143909199e666f9650

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page