[2B5-GS-2] Machine learning: Reinforcement learning

[2B5-GS-2-05] Mutual Optimization of Vehicle Groups and Traffic Signals Using Mean Field Approximation

〇Takumi Saiki¹, Sachiyo Arai¹ (1. Chiba University)

Keywords:Reinforcement Learning, Traffic Signal Control, Connected Car

Traffic signals are the primary method for facilitating vehicular traffic, but advances in automotive technology have increased the need for new methods. Deep reinforcement learning has recently attracted attention as an optimization method for traffic signals, and various methods have been proposed.
Most conventional methods focus only on traffic signals and assume that traffic flow is stationary regardless of the strategy. However, in an environment where congestion changes due to policy changes, vehicles may change course to less congested roads, resulting in changes in traffic flow beyond those predicted during learning. Technological developments related to connected cars will enable optimal routing according to various traffic conditions, and changes in signal programs are expected to affect multiple traffic flows. Therefore, it is necessary to develop signal control methods that consider changes in traffic flow due to signal optimization and signal programs.
However, considering route optimization for each vehicle would increase the number of agents and complicate the computation. In this study, we first formulate vehicle agents by a mean-field approximation. Then, we propose a method for stepwise optimization of traffic flow in an environment where vehicles and traffic signals learn in both directions.