Metadata-Version: 2.1
Name: jaxagents
Version: 0.1.2
Summary: Implementation of Reinforcement Learning agents in JAX
Home-page: https://github.com/amavrits/jax-agents
Author: Antonis Mavritsakis
Author-email: amavrits@gmail.com
License: MIT
Requires-Python: >=3.9.0
Description-Content-Type: text/markdown
Provides-Extra: dev
License-File: LICENSE.txt

# Jaxagents

Jaxagents is a Python implementation of Reinforcement Learning agents built upon JAX.

## Content

So far, the project includes the following agents:
* Q-learning:
  * Deep Q Networks (DQN)
  * Double Deep Q Networks (DDQN) 
  * Categorical Deep Q Networks (often known as C51)
  * Quantile Regression Deep Q Networks (QRDQN) 
* Policy gradient:
  * REINFORCE

## Background

Research and development in Reinforcement Learning can be computationally cumbersome. Utilizing JAX's high computational performance, Jaxagents provides a framework for applying and developing Reinforcement Learning agents that offers benefits in:
* computational speed
* easy control of random number generation
* hyperparameter optimization (via parallelized calculations)
