# Adaptive Traffic Control with Deep Reinforcement Learning: Towards State-of-the-art and Beyond

arXiv: https://arxiv.org/abs/2007.10960 · HTML: https://mlsanity.com/paper/2007.10960/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2020.

## Built on by (most cited first, 3 of 3)

- [Deep Reinforcement Q-Learning for Intelligent Traffic Signal Control with Partial Detection](https://mlsanity.com/paper/2109.14337/) (2021)
- [Cooperative Reinforcement Learning on Traffic Signal Control](https://mlsanity.com/paper/2205.11291/) (2022)
- [Cooperative Multi-Objective Reinforcement Learning for Traffic Signal Control and Carbon Emission Reduction](https://mlsanity.com/paper/2306.09662/) (2023)

## Abstract (arXiv)

In this work, we study adaptive data-guided traffic planning and control using Reinforcement Learning (RL). We shift from the plain use of classic methods towards state-of-the-art in deep RL community. We embed several recent techniques in our algorithm that improve the original Deep Q-Networks (DQN) for discrete control and discuss the traffic-related interpretations that follow. We propose a novel DQN-based algorithm for Traffic Control (called TC-DQN+) as a tool for fast and more reliable traffic decision-making. We introduce a new form of reward function which is further discussed using illustrative examples with comparisons to traditional traffic control methods.