Optimizing Traffic Lights with Multi-agent Deep Reinforcement Learning and V2X communication

TL;DR

This paper presents a multi-agent deep reinforcement learning system utilizing V2X communication to optimize traffic light durations, significantly reducing vehicle waiting times in urban traffic simulations.

Contribution

It introduces a novel multi-agent DRL framework with various reward functions and V2X data integration for traffic signal optimization.

Findings

Reduced average waiting cars by 41.5% compared to traditional systems.

Demonstrated effectiveness of shared and unshared reward functions in traffic control.

Validated system performance using SUMO traffic simulation.

Abstract

We consider a system to optimize duration of traffic signals using multi-agent deep reinforcement learning and Vehicle-to-Everything (V2X) communication. This system aims at analyzing independent and shared rewards for multi-agents to control duration of traffic lights. A learning agent traffic light gets information along its lanes within a circular V2X coverage. The duration cycles of traffic light are modeled as Markov decision Processes. We investigate four variations of reward functions. The first two are unshared-rewards: based on waiting number, and waiting time of vehicles between two cycles of traffic light. The third and fourth functions are: shared-rewards based on waiting cars, and waiting time for all agents. Each agent has a memory for optimization through target network and prioritized experience replay. We evaluate multi-agents through the Simulation of Urban MObility (SUMO) simulator. The results prove effectiveness of the proposed system to optimize traffic signals and reduce average waiting cars to 41.5 % as compared to the traditional periodic traffic control system.