ROS2Learn: a reinforcement learning framework for ROS 2
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We propose a novel framework for Deep Reinforcement Learning (DRL) in modular robotics that provides an approach which trains 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 compare the robustness of the performance of such methods in modular robots with an empirical study in simulation.
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