Reinforcement learning based on multi-agent in RoboCup

Abstract

Multi-agent systems form a particular type of distributed artificial intelligence systems. As an important character of players in game, autonomous agents' learning has become the main direction of researchers. In this paper, based on basic reinforcement learning, multi-agent reinforcement learning with specific context is proposed. The method is applied to RoboCup to learn coordination among agents. In the learning, the game field is divided into different areas, and the action choice is made dependent on the area in which the ball is currently located. This makes the state space and the action space decrease. After learning the optimal joint policy is determined. Comparison experiment between stochastic policy and this optimal policy shows the effectiveness of our approach. © Springer-Verlag Berlin Heidelberg 2005.