# Intelligent Coordination among Multiple Traffic Intersections Using Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/1912.03851 · HTML: https://mlsanity.com/paper/1912.03851/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2019.

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

- [Intelligent Transportation Systems to Mitigate Road Traffic Congestion](https://mlsanity.com/paper/2106.02315/) (2021)

## Abstract (arXiv)

We use Asynchronous Advantage Actor Critic (A3C) for implementing an AI agent in the controllers that optimize flow of traffic across a single intersection and then extend it to multiple intersections by considering a multi-agent setting. We explore three different methodologies to address the multi-agent problem - (1) use of asynchronous property of A3C to control multiple intersections using a single agent (2) utilise self/competitive play among independent agents across multiple intersections and (3) ingest a global reward function among agents to introduce cooperative behavior between intersections. We observe that (1) & (2) leads to a reduction in traffic congestion. Additionally the use of (3) with (1) & (2) led to a further reduction in congestion.