Cooperative strategy learning in multi-agent environment with continuous state space

Reinforcement learning is a powerful method for solving sequential decision making problems. But it is difficult to apply to practical problems such as multi-agent systems with continuous state space problems. In this paper we present a cooperative strategy learning method to solve the problem. It combines WOLF-PHC algorithms with function approximation of RL techniques. By this method an agent could learn cooperative behavior in the multi-agent environment with continuous state space. Using a subtask of RoboCup soccer, Keepaway, we demonstrate the effective of this learning method and the experiment results show that the algorithm converges.