Adaptive multi-objective reinforcement learning with hybrid exploration for traffic signal control based on cooperative multi-agent framework
In this paper, we focus on computing a consistent traffic signal configuration at each junction that optimizes multiple performance indices, i.e., multi-objective traffic signal control. The multi-objective function includes minimizing trip waiting time, total trip time, and junction waiting time. Moreover, the multi-objective function includes maximizing flow rate, satisfying green waves for platoons traveling in main roads, avoiding accidents especially in residential areas, and forcing vehicles to move within moderate speed range of minimum fuel consumption. In particular, we formulate our multi-objective traffic signal control as a multi-agent system (MAS). Traffic signal controllers have a distributed nature in which each traffic signal agent acts individually and possibly cooperatively in a MAS. In addition, agents act autonomously according to the current traffic situation without any human intervention. Thus, we develop a multi-agent multi-objective reinforcement learning (RL) traffic signal control framework that simulates the driver's behavior (acceleration/deceleration) continuously in space and time dimensions. The proposed framework is based on a multi-objective sequential decision making process whose parameters are estimated based on the Bayesian interpretation of probability. Using this interpretation together with a novel adaptive cooperative exploration technique, the proposed traffic signal controller can make real-time adaptation in the sense that it responds effectively to the changing road dynamics. These road dynamics are simulated by the Green Light District (GLD) vehicle traffic simulator that is the testbed of our traffic signal control. We have implemented the Intelligent Driver Model (IDM) acceleration model in the GLD traffic simulator. The change in road conditions is modeled by varying the traffic demand probability distribution and adapting the IDM parameters to the adverse weather conditions. Under the congested and free traffic situations, the proposed multi-objective controller significantly outperforms the underlying single objective controller which only minimizes the trip waiting time (i.e., the total waiting time in the whole vehicle trip rather than at a specific junction). For instance, the average trip and waiting times are ?8 and 6 times lower respectively when using the multi-objective controller.
reinforcement learning exploration traffic signal control adaptive optimization multi-objective optimization cooperative multi-agent system