Multi-Agent Q-learning in RoboCup Based on Regional Cooperative
LI Long-shu · Jisuanji gongcheng · 2009
Many multi-Agent Q-learning problems can not be solved because the number of joint actions is exponential in the number of Agents,rendering this approach infeasible for most problems.This paper investigates a regional cooperative of the Q-function by only considering the joint actions in those states in which coordination is actually required.In all other states single-Agent Q-learning is applied.This paper offers a compact state-action value representation,without compromising much in terms of solution quality.It performs experiments in RoboCup-simulation 2D which is the ideal testing platform of multi-agent systems and compared the algorithm to other multi-Agent reinforcement learning algorithms with promising results.