Implementing Traffic Signal Optimal Control by Multiagent Reinforcement Learning
In urban traffic environment, the traffic flow is more difficult to be predicted properly because the interaction and intertwinement among multiple crossroads, which makes preset traffic control model can not keep always high performance in all traffic situations. Considering the capability of autonomous learning being inherent in reinforcement learning, we propose a multiagent reinforcement learning based traffic signal control method. Without preset control model, multiple collaborative agents can learn the optimal control policy corresponding real traffic situation. The experiment results demonstrate the applicability and effectiveness of our approach.
traffic signal control multi-agent reinforcement learning autonomous learning multi-crossroads urban traffic trafficflow
Jiong Song Zhao Jin WenJun Zhu
Yunnan Jiao Tong Vocational and technical College Kunming, 650101, China Yunnan University Kunming, 650091, China
国际会议
哈尔滨
英文
2578-2582
2011-12-24（万方平台首次上网日期，不代表论文的发表时间）