Multi-agent Cooperative Reinforcement Learning Algorithm SE-MACOL and Its Application
Wenwei Chen · Journal of Guangxi Normal University · 2006
This paper extends Q-learning algorithm properly to multi-agent cooperative team domain,in which members make their decisions independently,and proposes a shared experience tuples multi-agent cooperative reinforcement learning algorithm.A new knowledge representation form composed of sequential pair as 〈state-value,action-value〉 is proposed,and experience tuples are shared with other agents in one team by through similarity transformation according to homogeneous subtasks.By importing this learning algorithm,not only the space of state-action is reduced,but also the learning efficiency is improved,and it shows that cooperation efficiency of the team is improved obviously.In the end,the algorithm is applied to pursuit game domain,and the result shows the validity of the algorithm that it can speed up the progress of pursuit task.