An Edge Based Multi-Agent Auto Communication Method for Traffic Light Control

With smart city infrastructures growing, the Internet of Things (IoT) has been widely

used in the intelligent transportation systems (ITS). The traditional adaptive traffic signal control

method based on reinforcement learning (RL) has expanded from one intersection to multiple

intersections. In this paper, we propose a multi-agent auto communication (MAAC) algorithm,

which is an innovative adaptive global traffic light control method based on multi-agent reinforcement

learning (MARL) and an auto communication protocol in edge computing architecture. The MAAC

algorithm combines multi-agent auto communication protocol with MARL, allowing an agent

to communicate the learned strategies with others for achieving global optimization in traffic

signal control. In addition, we present a practicable edge computing architecture for industrial

deployment on IoT, considering the limitations of the capabilities of network transmission bandwidth.

We demonstrate that our algorithm outperforms other methods over 17% in experiments in a real

traffic simulation environment.