Reinforcement Learning Ramp Metering without Complete Information

Xing-Ju Wang, Xiao-Ming Xi, Gui-Feng Gao
2012 Journal of Control Science and Engineering  
This paper develops a model of reinforcement learning ramp metering (RLRM) without complete information, which is applied to alleviate traffic congestions on ramps. RLRM consists of prediction tools depending on traffic flow simulation and optimal choice model based on reinforcement learning theories. Moreover, it is also a dynamic process with abilities of automaticity, memory and performance feedback. Numerical cases are given in this study to demonstrate RLRM such as calculating outflow
more » ... density, average speed, and travel time compared to no control and fixed-time control. Results indicate that the greater is the inflow, the more is the effect. In addition, the stability of RLRM is better than fixed-time control.
doi:10.1155/2012/208456 fatcat:wl5r3dnwxrbqbjmyfwsygmgwkq