A NEW LEARNING ALGORITHM FOR COOPERATIVE AGENTS IN GENERAL-SUM GAMES
The development of multi-agent reinforcement learning in stochastic game has been slowed down in recent years.The main problem is that it is difficult to make the learning satisfy rationality and convergence at the same time.Here, the typical learning algorithms are analyzed firstly, and then a new method called Pareto-Q is prompted with the concept of Pareto optimum, which is rational.At the same time, social conventions are also introduced to promise the convergence of learning.At the last, experiments are presented to prove the good learning result of this algorithm.
MAS Reinforcement learning Pareto optimum Social conventions
MEI-PING SONG JU-BAI AN RONG CHEN
College of Computer Science and Technology, Dalian Maritime University, China, 116026
国际会议
2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)
香港
英文
50-54
2007-08-19(万方平台首次上网日期,不代表论文的发表时间)