%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