Reinforcement Learning On-Ramp Metering with or without Complete Information

This paper discusses the congestion problem on highways and how ramp metering is used to alleviate this problem. It also gives an overview of the reinforcement learning ramp metering (RLRM) without and with complete information. RLRM is an Artificial Intelligence local-feedback control algorithm that adjusts the metering rate to keep the jam density of the on-ramp at a pre-specified level. RLRM uses reinforcement learning regulation to maintain a desired level of traffic density, or the target density, which is usually chosen to be the critical density or jam density. Numerical simulation experiment showed when effective reinforcement learning ramp metering without complete information is completed in the on-ramp section, traffic confusion can be prevented in advance and smooth traffic flow can be provided.