# CoLight: Learning Network-level Cooperation for Traffic Signal Control

## TL;DR

CoLight introduces a graph attentional network-based reinforcement learning model for adaptive traffic signal control, enabling dynamic cooperation among intersections to improve traffic flow in large-scale networks.

## Contribution

First application of graph attentional networks in reinforcement learning for traffic signal control, allowing dynamic, index-free communication among intersections.

## Key findings

- Outperforms state-of-the-art methods in large-scale experiments
- Effectively models temporal and spatial influences of neighboring intersections
- Demonstrates superior traffic flow improvements

## Abstract

Cooperation among the traffic signals enables vehicles to move through intersections more quickly. Conventional transportation approaches implement cooperation by pre-calculating the offsets between two intersections. Such pre-calculated offsets are not suitable for dynamic traffic environments. To enable cooperation of traffic signals, in this paper, we propose a model, CoLight, which uses graph attentional networks to facilitate communication. Specifically, for a target intersection in a network, CoLight can not only incorporate the temporal and spatial influences of neighboring intersections to the target intersection, but also build up index-free modeling of neighboring intersections. To the best of our knowledge, we are the first to use graph attentional networks in the setting of reinforcement learning for traffic signal control and to conduct experiments on the large-scale road network with hundreds of traffic signals. In experiments, we demonstrate that by learning the communication, the proposed model can achieve superior performance against the state-of-the-art methods.