# Efficiency and Equity are Both Essential: A Generalized Traffic Signal Controller with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2003.04046 · HTML: https://mlsanity.com/paper/2003.04046/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2020.

## Built on by (most cited first, 5 of 5)

- [Learning Hierarchical Interactive Multi-Object Search for Mobile Manipulation](https://mlsanity.com/paper/2307.06125/) (2023)
- [CVLight: Decentralized Learning for Adaptive Traffic Signal Control with Connected Vehicles](https://mlsanity.com/paper/2104.10340/) (2021)
- [Courteous Behavior of Automated Vehicles at Unsignalized Intersections via Reinforcement Learning](https://mlsanity.com/paper/2106.06369/) (2021)
- [Deep Reinforcement Q-Learning for Intelligent Traffic Signal Control with Partial Detection](https://mlsanity.com/paper/2109.14337/) (2021)
- [Agent-Agnostic Centralized Training for Decentralized Multi-Agent Cooperative Driving](https://mlsanity.com/paper/2403.11914/) (2024)

## Abstract (arXiv)

Traffic signal controllers play an essential role in today's traffic system. However, the majority of them currently is not sufficiently flexible or adaptive to generate optimal traffic schedules. In this paper we present an approach to learning policies for signal controllers using deep reinforcement learning aiming for optimized traffic flow. Our method uses a novel formulation of the reward function that simultaneously considers efficiency and equity. We furthermore present a general approach to find the bound for the proposed equity factor and we introduce the adaptive discounting approach that greatly stabilizes learning and helps to maintain a high flexibility of green light duration. The experimental evaluations on both simulated and real-world data demonstrate that our proposed algorithm achieves state-of-the-art performance (previously held by traditional non-learning methods) on a wide range of traffic situations.