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

arXiv: https://arxiv.org/abs/2002.09853 · HTML: https://mlsanity.com/paper/2002.09853/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2020.

## Built on by (most cited first, 1 of 1)

- [CVLight: Decentralized Learning for Adaptive Traffic Signal Control with Connected Vehicles](https://mlsanity.com/paper/2104.10340/) (2021)

## Abstract (arXiv)

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.