We propose a novel framework for Deep Reinforcement Learning (DRL) in modular robotics to train a robot directly from joint states, using traditional robotic tools. We use an state-of-the-art implementation of the Proximal Policy Optimization, Trust Region Policy Optimization and Actor-Critic Kronecker-Factored Trust Region algorithms to learn policies in four different Modular Articulated Robotic Arm (MARA) environments. We support this process using a framework that communicates with typical tools used in robotics, such as Gazebo and Robot Operating System 2 (ROS 2). We evaluate several algorithms in modular robots with an empirical study in simulation.
@article{arxiv.1903.06282,
title = {ROS2Learn: a reinforcement learning framework for ROS 2},
author = {Yue Leire Erro Nuin and Nestor Gonzalez Lopez and Elias Barba Moral and Lander Usategui San Juan and Alejandro Solano Rueda and Víctor Mayoral Vilches and Risto Kojcev},
journal= {arXiv preprint arXiv:1903.06282},
year = {2019}
}