COOPERATIVE STRATEGY LEARNING IN MULTI-AGENT ENVIRONMENT WITH CONTINUOUS STATE SPACE

摘要：

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.

关键词： Reinforcement learning multi-agent cooperative behavior continuous state space

作者: JUN-YUAN TAO DE-SHENG LI

作者单位: Department of Automatic Measurement and Control, Harbin Institute of Technology, Harbin, China Department of Mechanical and Electronic Engineering, Beijing University of Technology, Beijing, Chin

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 2107-2111

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）