%0 Journal Article %A MENG Xiang-ping %A WANG Sheng-bin %A WANG Xin-xin %T Multiagent Q-learning based on ant colony algorithm and roulette algorithm %D 2009 %R 10.3778/j.issn.1002-8331.2009.16.016 %J Computer Engineering and Applications %P 60-62 %V 45 %N 16 %X Authors present a novel Multiagent Reinforcement Learning Algorithm based on Q-Learning,ant colony algorithm and roulette algorithm.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,the next action is restrainted seriously by the high Q value,in the prophase.So,authors combine the ant conlony algorithm,roulette algorithm with Q-learning,hope that the problems will be resolved with the algorithm proposed.At last,the theory analysis and experiment result both demonstrate that the improved Q-learning is feasible and increases the learning efficiency. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2009.16.016