一般和对策中基于协商的多代理强化学习  

Multiagent Reinforcement Learning Based on Negotiation in General-Sum Games

作　　者：张化祥[1] 赵彤[2] 黄上腾[3] 

机构地区：[1]山东师范大学信息管理学院,山东济南250014 [2]青岛科技大学自动化与电子工程学院,山东青岛266061 [3]上海交通大学计算机科学与工程系,上海200030

出　　处：《上海交通大学学报》2005年第S1期108-112,共5页Journal of Shanghai Jiaotong University

摘　　要：一般和对策中,只考虑个体理性的多代理协作是一种无全局目标的协作.代理学习基于对手策略假设,不能保证假设的正确性.为此通过定义代理协作的集体目标,提出了一种基于多代理协商的代理强化学习算法.代理选择协商策略,并惩罚偏离该策略的代理来保证协商策略的执行.文中给出了学习收敛的条件及证明,并以实例加以分析.In general-sum games, multiagent cooperation has no global objective, and only individual rationality is concerned. Agent s learning is based on the assumption of opponents policies, and this assumption may be wrong. By defining the global objective of agents, a novel multiagent reinforcement learning algorithm was proposed. All agents selected negotiated policies during learning, and punished those agents deviating from negotiated policies to ensure the execution of these policies. It was proved that the ...

关 键 词：MARKOV对策 强化学习 多代理协作 协商 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]