关键词: 随机对策, Q-学习, 实时性, 局部合作, RoboCup仿真2D, 配合策略

Abstract: Because many multi-Agent cooperative problems can hardly be solved in RoboCup, this paper investigates a regional cooperative multi-Agent Q-learning method. Through subdividing the stadium area and rewards of agents, the agents’ collaboration ability can be strengthened. As a result, the team’s offensive and defensive abilities are enhanced. At the same time, the agents can spend less time learning via restricting the using range of the algorithm. Consequently, the real-time of the game can be ensured. Finally, the experiment on the platform of the simulation 2D proves that the effect of this method is much better than that of the previous one, and it fully complies with the design of the original goal.

Key words: stochastic game, Q-learning, real-time, regional cooperation, RoboCup simulation 2D, cooperative strategy