Reinforcement Learning Algorithm for Dynamic Policy Under Mixed Multi-agent Domains
Shiyong Zhang · Journal of Chinese Computer Systems · 2009
Recently machine learning is paid much attention to and researched more deeply in collaboration and action selection of multi-agent systems.In this paper we analyzed equilibrium based and best response based learning algorithms,and proposed two reinforcement learning algorithms for dynamic policy under mixed multi-agent domains.These algorithms not only can adapt to policy and its variation of other agents,but also can make out more accurate time-related policy using past behavior history.Based on two well-known zero-sum games,convergence and rationality of this algorithm is validated,and it can receive higher utility in repeated games against best response based agents.