Metadata-Version: 2.1
Name: farmyard
Version: 0.0.1.dev3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Rust
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Requires-Dist: pip>=21
Requires-Dist: numpy~=1.20
Requires-Dist: pytest>=6.2.4; extra == 'test'
Requires-Dist: pytest-cov>=2.12.1; extra == 'test'
Provides-Extra: test
License-File: LICENSE
Summary: The Minimal Reinforcement Learning Framework
Author: Nathan Michlo <nathanjmichlo@gmail.com>
Author-email: Nathan Juraj Michlo <NathanJMichlo@gmail.com>
Requires-Python: >=3.8
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM
Project-URL: homepage, https://github.com/nmichlo/farmyard
Project-URL: repository, https://github.com/nmichlo/farmyard


<p align="center">
    <h1 align="center">🌾 Farmyard 🐄</h1>
    <p align="center">
        <i>The Minimal Reinforcement Learning Framework</i>
    </p>
</p>

<p align="center">
    <a href="https://choosealicense.com/licenses/mit/" target="_blank">
        <img alt="license" src="https://img.shields.io/github/license/nmichlo/farmyard?style=flat-square&color=lightgrey"/>
    </a>
    <a href="https://pypi.org/project/farmyard" target="_blank">
        <img alt="python versions" src="https://img.shields.io/pypi/pyversions/farmyard?style=flat-square"/>
    </a>
    <a href="https://pypi.org/project/farmyard" target="_blank">
        <img alt="pypi version" src="https://img.shields.io/pypi/v/farmyard?style=flat-square&color=blue"/>
    </a>
    <a href="https://github.com/nmichlo/farmyard/actions?query=workflow%3Atest">
        <img alt="tests status" src="https://img.shields.io/github/workflow/status/nmichlo/farmyard/test?label=tests&style=flat-square"/>
    </a>
    <a href="https://codecov.io/gh/nmichlo/farmyard/">
        <img alt="code coverage" src="https://img.shields.io/codecov/c/gh/nmichlo/farmyard?token=86IZK3J038&style=flat-square"/>
    </a>
</p>

<p align="center">
    <p align="center">
        <a href="https://github.com/nmichlo/farmyard/issues/new/choose">Contributions</a> are welcome!
    </p>
</p>

----------------------

## Table Of Contents

- [Overview](#overview)
- [Roadmap](#roadmap)

----------------------

## Overview

The minimal reinforcement learning framework.

Get started with `farmyard` by installing it with $`pip install farmyard` or cloning this repository.

## Roadmap

This project is being built while I learn about reinforcement
learning, the roadmap for the project includes:
- Common RL Algorithms (Easily Comparable)
- Common RL Performance Tricks (Separate from Algorithms)
- Common RL Models
- Flexible/Modular Components
- Multiprocessing
- Distributed Computing

