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.