关键词: 多智能体, 增强学习, 机器人世界杯足球锦标赛

Abstract: Due to the presence of other agents，the environment of Multi-Agent Systems（MAS） cannot be simply treated as Markov Decision Processes（MDPs）.The current reinforcement learning which are based on MDPs must be reformed before it can be applicable to MAS.Based on an agent’s independent learning ability，this paper proposes a novel Q-learning algorithm for MAS-an agent learning other agents action policies through observing the joint action.The politicies of other agents are expressed as action probability distribution matrixes.A concise and yet useful updating method for the matrixes is proposed.The full joint probability of distribution matrixes guarantees the learning agent to choose its optimal action.In experiment，the implemention of the agent and the enhancement of AFU shows that the approach is valid and efficient.

Key words: Multi-Agents Systems（MAS）, reinforcement learning, Robot World Cup（RoboCup）