Distributed agent-based deep reinforcement learning for large scale traffic signal control

Traffic signal control (TSC) is an established yet challenging engineering solution that alleviates traffic congestion by coordinating vehicles’ movements at road intersections. Theoretically, reinforcement learning (RL) is a promising method for adaptive TSC in complex urban traffic networks. However, current TSC systems still rely heavily on simplified rule-based methods in practice. In this paper, we propose: (1) two game theory-aided RL algorithms leveraging Nash Equilibrium and RL, namely Nash Advantage Actor–Critic (Nash-A2C) and Nash Asynchronous Advantage Actor–Critic (Nash-A3C); (2) a distributed computing Internet of Things (IoT) architecture for traffic simulation, which is more suitable for distributed TSC methods like the Nash-A3C deployment in its fog layer. We apply both methods in our computing architecture and obtain better performance than benchmark TSC methods by 22.1% and 9.7% reduction of congestion time and network delay, respectively.