XCS-based reinforcement learning algorithm for motion planning of a spherical mobile robot

摘要

A Reinforcement Learning (RL) algorithm based on eXtended Classifier System (XCS) is used to navigate a spherical robot. Traditional motion planning strategies rely on pre-planned optimal trajectories and feedback control techniques. The proposed learning agent approach enjoys a direct model-free methodology that enables the robot to function in dynamic and/or partially observable environments. The agent uses a set of guard-action rules that determines the motion inputs at each step. Using a number of control inputs (actions) and the developed RL scheme, the agent learns to make near-optimal moves in response to the incoming position/orientation signals. The proposed method employs an improved variant of the XCS as its learning agent. Results of several simulated experiments for the spherical robot show that this approach is capable of planning a near-optimal path to a predefined target from any given position/orientation.