Modular Q-learning based multi-agent cooperation for robot soccer

In a multi-agent system, action selection is important for the cooperation and coordination among agents. As the environment is dynamic and complex, modular Q-learning, which is one of the reinforcement learning schemes, is employed in assigning a proper action to an agent in the multi-agent system. The architecture of modular Q-learning consists of learning modules and a mediator module. The mediator module of the modular Q-learning system selects a proper action for the agent based on the Q-value obtained from each learning module. To obtain better performance, along with the Q-value, the mediator module also considers the state information in the action selection process. A uni-vector field is used for robot navigation. In the robot soccer environment, the effectiveness and applicability of modular Q-learning and the uni-vector field method are verified by real experiments using five micro-robots. © 2001 Elsevier Science B.V. All rights reserved.