Traffic Signal Control Agent Interaction Model based on Game Theory and Reinforcement Learning
Game theory is the best mathematical tool to study human society interaction. Learning approach has an important influence on interaction. The Traffic Signal Control Agent (TSCA) interaction model is the basis of research on coordinated control of urban area traffic signal .As for the interactive intersection, this research constructed structure models of two TSCAs, such as intersection Agent and management Agent. Based on this, the TSCA interactive frame model was established. This research constructed TSCA interaction mathematical model via game theory. In the interaction mathematical model, the renewed Q-values in the distributed reinforcement Q-learning was used to build the payoff values. Therefore, interaction has taken on from the action selection between TSCAs. Next , an algorithm of distributed Q-learning based on distributed weight function is brought forward. The interaction model paves the way for traffic control simulation in the future.
intersection Agent interaction reinforcement learning game theory
Xia Xinhai Xu Lunhui
School of Civil Engineering and Transportation, South China University of Technology (SCUT), Guangzh School of Civil Engineering and Transportation, South China University of Technology (SCUT), Guangzh
国际会议
重庆
英文
164-168
2009-12-25（万方平台首次上网日期，不代表论文的发表时间）