Multi-agent deep reinforcement learning with traffic flow for traffic signal control

Abstract

Multi-agent Reinforcement Learning (MARL) has become one of the best methods in Adaptive Traffic Signal Control (ATSC). Traffic flow is a very regular traffic volume, which is highly critical to signal control policy. However, dynamic control policies will directly affect traffic flow formation, and it is impossible to provide observation through the original traffic flow prediction. This paper proposes a method for estimating traffic flow according to the time window in Reinforcement Learning (RL) training. Therefore, it is verified on both the regular road network and the real road network. Our method further reduces the intersection delay and queue length compared with the original method.