# Deep Reinforcement Q-Learning for Intelligent Traffic Signal Control with Partial Detection

arXiv: https://arxiv.org/abs/2109.14337 · HTML: https://mlsanity.com/paper/2109.14337/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 38 references that are in the map.
- First posted on arXiv 29 Sep 2021.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [CoLight: Learning Network-level Cooperation for Traffic Signal Control](https://mlsanity.com/paper/1905.05717/) (2019)
- [Learning Phase Competition for Traffic Signal Control](https://mlsanity.com/paper/1905.04722/) (2019)
- [A Survey on Traffic Signal Control Methods](https://mlsanity.com/paper/1904.08117/) (2019)
- [Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks](https://mlsanity.com/paper/1803.11115/) (2018)
- [Adaptive Traffic Signal Control: Deep Reinforcement Learning Algorithm with Experience Replay and Target Network](https://mlsanity.com/paper/1705.02755/) (2017)
- [Using Reinforcement Learning with Partial Vehicle Detection for Intelligent Traffic Signal Control](https://mlsanity.com/paper/1807.01628/) (2018)
- [An Open-Source Framework for Adaptive Traffic Signal Control](https://mlsanity.com/paper/1909.00395/) (2019)
- [Efficiency and Equity are Both Essential: A Generalized Traffic Signal Controller with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.04046/) (2020)
- [Partially Detected Intelligent Traffic Signal Control: Environmental Adaptation](https://mlsanity.com/paper/1910.10808/) (2019)
- [Adaptive Traffic Control with Deep Reinforcement Learning: Towards State-of-the-art and Beyond](https://mlsanity.com/paper/2007.10960/) (2020)
- [A self-organizing system for urban traffic control based on predictive interval microscopic model](https://mlsanity.com/paper/1406.1128/) (2014)
- [Estimation of urban traffic state with probe vehicles](https://mlsanity.com/paper/1811.05394/) (2018)

## Built on by (most cited first, 4 of 4)

- [Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning](https://mlsanity.com/paper/2308.14284/) (2023)
- [A Simple Framework Towards Vision-based Traffic Signal Control with Microscopic Simulation](https://mlsanity.com/paper/2403.06884/) (2024)
- [An LLM-Explainable DRL Framework for Passenger-Directed Autonomous Driving](https://mlsanity.com/paper/2606.20640/) (2026)
- [Reinforcement Learning-Based Control of CAV Platoon Joining Maneuvers in Mixed Traffic](https://mlsanity.com/paper/2608.26860/) (2026)

## Abstract (arXiv)

Intelligent traffic signal controllers, applying DQN algorithms to traffic light policy optimization, efficiently reduce traffic congestion by adjusting traffic signals to real-time traffic. Most propositions in the literature however consider that all vehicles at the intersection are detected, an unrealistic scenario. Recently, new wireless communication technologies have enabled cost-efficient detection of connected vehicles by infrastructures. With only a small fraction of the total fleet currently equipped, methods able to perform under low detection rates are desirable. In this paper, we propose a deep reinforcement Q-learning model to optimize traffic signal control at an isolated intersection, in a partially observable environment with connected vehicles. First, we present the novel DQN model within the RL framework. We introduce a new state representation for partially observable environments and a new reward function for traffic signal control, and provide a network architecture and tuned hyper-parameters. Second, we evaluate the performances of the model in numerical simulations on multiple scenarios, in two steps. At first in full detection against existing actuated controllers, then in partial detection with loss estimates for proportions of connected vehicles. Finally, from the obtained results, we define thresholds for detection rates with acceptable and optimal performance levels.