Sharing Control Knowledge Among Heterogeneous Intersections: A Distributed Arterial Traffic Signal Coordination Method Using Multi-Agent Reinforcement Learning
Hong Zhu, J. H. Feng, Fengmei Sun, Keshuang Tang, Di Zang, Qi Kang
Abstract
Treating each intersection as basic agent, multi-agent reinforcement learning (MARL) methods have emerged as the predominant approach for distributed adaptive traffic signal control (ATSC) in multi-intersection scenarios, such as arterial coordination. MARL-based ATSC currently faces two challenges: disturbances from the control policies of other intersections may impair the learning and control stability of the agents; and the heterogeneous features across intersections may complicate coordination efforts. To address these challenges, this study proposes a novel MARL method for distributed ATSC in arterials, termed the Distributed Controller for Heterogeneous Intersections (DCHI). The DCHI method introduces a Neighborhood Experience Sharing (NES) framework, wherein each agent utilizes both local data and shared experiences from adjacent intersections to improve its control policy. Within this framework, the neural networks of each agent are partitioned into two parts following the Knowledge Homogenizing Encapsulation (KHE) mechanism. The first part manages heterogeneous intersection features and transforms the control experiences, while the second part optimizes homogeneous control logic. Experimental results demonstrate that the proposed DCHI achieves efficiency improvements in average travel time of over 30% compared to traditional methods and yields similar performance to the centralized sharing method. Furthermore, vehicle trajectories reveal that DCHI can adaptively establish green wave bands in a distributed manner. Given its superior control performance, accommodation of heterogeneous intersections, and low reliance on information networks, DCHI could significantly advance the application of MARL-based ATSC methods in practice.