Abstract
The present paper proposes a new reinforcement learning (RL) system called a state predictor-based RL system in order to solve the explosion of state space and create cooperative behaviors in multiagent systems. The proposed system realizes a predictive function by representing both the present and the next state-action groups with ITPM, an incremental topology map. The proposed system is applied to the pursuit problem, and its performance is evaluated by comparison with the conventional RL method in computer simulations. The experimental results show that the proposed system can appropriately learn in a complex environment which is hardly solved by the conventional RL. Furthermore, it is confirmed that the proposed system can acquire cooperative strategies. © 2010 Wiley Periodicals, Inc. Electron Comm Jpn, 93(6): 8–18, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecj.10258
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