Learning to Coordinate Traffic Signals With Adaptive Network Partition

Multi-intersection traffic signal control (TSC) is an active research field in multi-agent systems, where traffic signals for each intersection, controlled by an agent, must coordinate to optimize traffic flow. To encourage global coordination, previous work partitions the traffic network into several regions and learns policies for agents in a feudal structure. However, static network partition fails to adapt to dynamic traffic flow, which changes frequently over time. To address this, we propose a novel multi-agent reinforcement learning approach with adaptive network partition. Specifically, we partition the network into several regions according to the dynamic traffic flow over time. To do this, we propose two approaches: one is directly to use graphic neural network (GNN) to generate the network partition, and the other is to use Monte-Carlo tree search (MCTS) to find the best partition with criteria computed by GNN. Then, we design a variant of Qmix using GNN to handle various dimensions of input, given by the dynamic network partition. Finally, we use a feudal hierarchy to manage agents in each partition and promote global cooperation. By doing so, agents are able to adapt to the traffic flow as required in practice. We empirically evaluate our method both in a synthetic traffic grid and real-world traffic networks of three cities, widely used in the literature. The experimental results confirm that our method achieved better performance both in a synthetic traffic grid and real-world traffic networks of three cities, in terms of average travel time and queue length, than several leading TSC baselines.