Cooperative Multi-agent Systems Based on Reinforcement Learning
Zheng Shu · Mini-micro Systems · 2003
Reinforcement learning can provide a robust and natural means for agents to learn how to coordinate their action choices in fully cooperative multi agent systems (MAS). This paper first introduces the basic principles and components of reinforcement learning, then describes multi agent extension MMDP and presents reinforcement learning model of agents in cooperative MAS. After that we distinguish reinforcement learners that ignore the presence of other agents from those that explicitly attempt to learn the value of joint actions and strategies of their counterparts. In the last, some simple and commonly used coordination mechanisms are examined.