Large-Scale Regional Traffic Signal Control Using Dynamic Deep Reinforcement Learning

Multi-agent Reinforcement Learning (MARL) based traffic signal control becomes a popular research topic in recent years. Most existing MARL approaches tend to learn the optimum control strategies in a decentralised manner by considering communication among neighbouring intersections. However, the non-stationary property in MARL may lead to extremely slow or even failure of convergence, especially when the number of intersections becomes large. One of the existing methods is to partition the whole network into several regions, each of which utilizes a centralized RL framework to speed up the convergence rate. However, there are two challenges for this strategy: the first one is how to get a flexible partition and the second one is how to search for the optimal joint actions for a region of intersections. In this paper, we propose a novel training framework where our region partitioning rule is based on the adjacency between the intersections and propose Dynamic Branching Dueling Q-Network (DBDQ) to search for optimal joint action efficiently and to maximize the regional reward. The experimental results with both real datasets and synthetic datasets demonstrate the superiority of our framework over other existing frameworks.