# Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learning

## TL;DR

This paper develops deep reinforcement learning algorithms, specifically policy-gradient and value-function based methods, to optimize traffic light control at intersections, demonstrating stable and promising results in a traffic simulation environment.

## Contribution

It introduces two novel deep reinforcement learning agents for traffic light control, combining policy-gradient and value-function approaches, with successful implementation in a traffic simulator.

## Key findings

- Stable training without instability issues.
- Effective traffic signal prediction in simulation.
- Potential for real-world traffic management improvements.

## Abstract

Recent advances in combining deep neural network architectures with reinforcement learning techniques have shown promising potential results in solving complex control problems with high dimensional state and action spaces. Inspired by these successes, in this paper, we build two kinds of reinforcement learning algorithms: deep policy-gradient and value-function based agents which can predict the best possible traffic signal for a traffic intersection. At each time step, these adaptive traffic light control agents receive a snapshot of the current state of a graphical traffic simulator and produce control signals. The policy-gradient based agent maps its observation directly to the control signal, however the value-function based agent first estimates values for all legal control signals. The agent then selects the optimal control action with the highest value. Our methods show promising results in a traffic network simulated in the SUMO traffic simulator, without suffering from instability issues during the training process.