Learning robotic soccer controllers with the Q-Batch update-rule

Abstract

Robotic soccer provides a rich environment for the development of Reinforcement Learning controllers. The competitive environment imposes strong requirements on performance of the developed controllers. RL offers a valuable alternative for the development of efficient controllers while avoiding the hassle of parameter tuning a hand coded policy. This paper presents the application of a recently proposed Batch RL update-rule to learn robotic soccer controllers in the context of the RoboCup Middle Size League. The Q-Batch update-rule exploits the episodic structure of the data collection phase of Batch RL to efficiently evaluate and improve the learned policy. Three different learning tasks, with increasing difficulty, were developed and applied on a simulated environment and later on the physical robot. The performance of the learned controllers is mostly compared to hand-tuned controllers while some comparisons with other RL methods were performed. Results show that the proposed approach is able to learn the tasks in a reduced amount of time, even outperforming existing hand-coded solutions.