Study for some problems of multi-agent Q-learning and improving
Xinxin Wang · Jisuanji gongcheng yu sheji · 2009
A novel multi-agent reinforcement learning algorithm based on Q-Learning,ant colony algorithm and roulette algorithm is presented.As in reinforcement learning algorithm,when the number of agents is large enough,all of the action selection methods will be failed:the speed of learning is decreased sharply.Besides,as the Agent makes use of the Q value to choose the next action so that the next action is restrained seriously by the high Q value,in the prophase.So,we try to combine the ant colony algorithm,roulette algorithm with Q-learning,hoping that the problems will be resolved with our proposed.At last,the theory analysis and result of experiment both demonstrate that the improved Q-learning is feasible and increase the learning efficiency.