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This paper introduces a multi-agent approach to adjust traffic lights basedon traffic situation in order to reduce average delay time. In the trafficmodel, lights of each intersection are controlled by an autonomous agent. Sincedecision of each agent affects neighbor agents, this approach creates aclassical non-stationary environment. Thus, each agent not only needs to learnfrom the past experience but also has to consider decision of neighbors toovercome dynamic changes of the traffic network. Fuzzy Q-learning and Gametheory are employed to make policy based on previous experiences and decisionof neighbor agents. Simulation results illustrate the advantage of the proposedmethod over fixed time, fuzzy, Q-learning and fuzzy Q-learning control methods.

Fuzzy Q-Learning Based Multi-Agent System for Intelligent Traffic Control by a Game Theory Approach