Extensible Hierarchical Multi-Agent Reinforcement-Learning Algorithm in Traffic Signal Control

Abstract

Reinforcement-learning (RL) algorithms have made great achievements in many scenarios. However, in large-scale traffic signal control (TSC) scenarios, RL still falls into local optima when controlling multiple signal lights. To solve this problem, we propose a novel goal-based multi-agent hierarchical model (GMHM). Specifically, we divide the traffic environment into several regions. The region contains a virtual manager and several workers who control the traffic lights. The manager assigns goals to each worker by observing the environment, and the worker makes decisions according to the environment state and the goal. For the worker, we adapted the goal-based multi-agent deep deterministic policy gradient (MADDPG) algorithm combined with hierarchical reinforcement learning. In this way, we simplify tasks and allow agents to cooperate more efficiently. We carried out experiments on both grid traffic scenarios and real-world scenarios in the SUMO simulator. The experimental results show the performance advantages of our algorithm compared with state-of-the-art algorithms.