Research on regional cooperative multi-agent Q-learning
LI Long-shu · Computer Engineering and Applications Journal · 2008
Reinforcement learning in Multi-Agent Systems suffers from the fact that both the state and the action space scale exponentially with the number of Agents,which also lead to low learning speed.In this paper,the authors investigate a regional cooperative of the Q-function by only considering the joint actions in those states in which coordination is actually requires.In all other states Single-Agent Q-learning is applies.This offers a compact state-action value representation,without compromising much in terms of solution quality.The authors have performed experiments in the predator-prey domain and robocup-simulation 2D which is the ideal testing platform of Multi-Agent Systems and compared this algorithm to other Multi-Agent reinforcement learning algorithms with promising results.