RoboCup-2D passing strategy based on joint reinforcement learning
Xiaojun Chang · Computer Engineering and Applications Journal · 2011
A combined Q-learning algorithm of Multi-Agent System(MAS) is proposed on the basis of the traditional Q-learning algorithm.Multi-agent learning is performed under the same evaluation function.While learning results of all the agents which participate in collaboration are taken into account during the learning process.The pitch components of state are reduced by introducing a state of decomposition method in RoboCup-2D soccer simulation game.The optimal state obtained by joint learning is adopted as the optimal action group of collaborative multi-agent.The problems of passing strategy and cooperation between all agents in the simulation are effective solved.The results of simulation and experiments demonstrate the validity and reliability of the proposed algorithm.