Extending the OpenAI Gym for robotics: a toolkit for reinforcement learning using ROS and Gazebo

Iker Zamora; Nestor Gonzalez Lopez; Victor Mayoral Vilches; Alejandro Hernandez Cordero

Extending the OpenAI Gym for robotics: a toolkit for reinforcement learning using ROS and Gazebo

Robotics 2017-02-08 v2

Authors: Iker Zamora , Nestor Gonzalez Lopez , Victor Mayoral Vilches , Alejandro Hernandez Cordero

Abstract

This paper presents an extension of the OpenAI Gym for robotics using the Robot Operating System (ROS) and the Gazebo simulator. The content discusses the software architecture proposed and the results obtained by using two Reinforcement Learning techniques: Q-Learning and Sarsa. Ultimately, the output of this work presents a benchmarking system for robotics that allows different techniques and algorithms to be compared using the same virtual conditions.

Keywords

OpenAI Gym robotics reinforcement learning ROS Gazebo benchmarking Q-Learning Sarsa

Cite

@article{arxiv.1608.05742,
  title  = {Extending the OpenAI Gym for robotics: a toolkit for reinforcement learning using ROS and Gazebo},
  author = {Iker Zamora and Nestor Gonzalez Lopez and Victor Mayoral Vilches and Alejandro Hernandez Cordero},
  journal= {arXiv preprint arXiv:1608.05742},
  year   = {2017}
}

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