Micro Junction Agent: A Scalable Multi-agent Reinforcement Learning Method for Traffic Control

Abstract

Traffic congestion is increasing steadily worldwide and many researchers have attempted to employ smart methods to control the traffic. One such approach is the multi-agent reinforcement learning (MARL) scheme wherein each agent corresponds to a moving entity such as vehicles. The aim is to make all mobile objects arrive at their target destination in the least amount of time without collision. However, as the number of vehicles increases, the computational complexity increases, and therefore computation cost increases, and scalability cannot be guaranteed. In this paper, we propose a novel approach using MARL, where the traffic junction becomes the agent. Each traffic junction is composed of four Micro Junction Agents (MJAs) and a MJA becomes the observer and the agent controlling all vehicles within the observation area. Results show that MJA outperforms other MARL techniques on various traffic junction scenarios.