Multi-agent Deep Reinforcement Learning collaborative Traffic Signal Control method considering intersection heterogeneity.

Traffic Signal Control (TSC) plays a crucial role in mitigating congestion. The extensive integration of Deep Reinforcement Learning (DRL) into TSC, fueled by the advancements in artificial intelligence, has yielded remarkable efficacy. Nevertheless, prevailing DRL models are typically developed within homogeneous road networks, characterized by identical intersection types and relatively balanced traffic flow patterns. This predominant approach often results in an oversight of the impact of intersection heterogeneity, encompassing variations in intersection geometric structure, traffic demands, signal timing design, and other pertinent factors, on action selections by researchers. This paper addresses the aforementioned gap by introducing a value-decomposition-based spatiotemporal graph attention multi-agent DRL model (MARL_SGAT) that expressly accommodates intersection heterogeneity. Specifically, a heterogeneous correlation index is designed using road network structural parameters and serves to formulate a heterogeneous reward function, enabling the quantification of action execution improvements in diverse road network environments. To address the intricate spatiotemporal features of traffic flows within the heterogeneous road network, the model strategically selects and incorporates these features during the formulation by leveraging a spatiotemporal graph attention network. On these bases, double dual networks are introduced to convert the individual-global-max constraint of action selection into a value range constraint of the action advantage function, thereby facilitating model learning. Simulation results showcase the superior performance of the MARL_SGAT algorithm when compared to seven baseline algorithms. Specifically, the algorithm manifests in reduced average vehicle delays, decreased number of stops, and elevated travel speeds within the controlled road network. These advantages are particularly conspicuous in heterogeneous road network environments. • MARL method for collaborative control of heterogeneous intersections is proposed. • A correlation index is formulated to quantify intersection topology variations. • A novel reward function is designed to offer more precise evaluation method. • The double dual networks simplify the learning process for Q value function. • Performance of the proposed model is validated using actual data set. [ABSTRACT FROM AUTHOR]