Documentation

This stack provides a ROS based framework for performing reinforcement learning (RL) and packages of RL agents and environments.

The code in this repository provides agents, environments, and multiple ways for them to communicate (through ROS messages, or by including the agent and environment libraries). There are 5 packages in the repository:

  • rl_common: Some files that are common to both agents and environments.

  • rl_msgs: Definitions of ROS messages for agents and envs to communicate.

  • rl_agent: A library of some RL agents including Q-Learning and TEXPLORE.

  • rl_env: A library of some RL environments such as Taxi and Fuel World.

  • rl_experiment: Code to run some RL experiments without ROS message passing.

More details for each of the packages are available at the above links. References for some of the algorithms and environments provided in this package are available at the author's website.

Checking out the code

Check out the code at: https://github.com/toddhester/rl-texplore-ros-pkg

Tutorial

For details on how to use this stack, check out the tutorial.

Features

Some of the key features of this package include:

References

Wiki: reinforcement_learning (last edited 2015-03-24 00:24:25 by ToddHester)