# Towards Robust Deep Reinforcement Learning for Traffic Signal Control: Demand Surges, Incidents and Sensor Failures

arXiv: https://arxiv.org/abs/1904.08353 · HTML: https://mlsanity.com/paper/1904.08353/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2019.

## Built on by (most cited first, 6 of 6)

- [The Real Deal: A Review of Challenges and Opportunities in Moving Reinforcement Learning-Based Traffic Signal Control Systems Towards Reality](https://mlsanity.com/paper/2206.11996/) (2022)
- [Improving the generalizability and robustness of large-scale traffic signal control](https://mlsanity.com/paper/2306.01925/) (2023)
- [MalLight: Influence-Aware Coordinated Traffic Signal Control for Traffic Signal Malfunctions](https://mlsanity.com/paper/2408.09768/) (2024)
- [A Methodology for the Development of RL-Based Adaptive Traffic Signal Controllers](https://mlsanity.com/paper/2101.09614/) (2021)
- [Antifragile perimeter control: Thriving on disruptions through reinforcement learning](https://mlsanity.com/paper/2402.12665/) (2024)
- [Robustness of Reinforcement Learning-Based Traffic Signal Control under Incidents: A Comparative Study](https://mlsanity.com/paper/2506.13836/) (2025)

## Abstract (arXiv)

Reinforcement learning (RL) constitutes a promising solution for alleviating the problem of traffic congestion. In particular, deep RL algorithms have been shown to produce adaptive traffic signal controllers that outperform conventional systems. However, in order to be reliable in highly dynamic urban areas, such controllers need to be robust with the respect to a series of exogenous sources of uncertainty. In this paper, we develop an open-source callback-based framework for promoting the flexible evaluation of different deep RL configurations under a traffic simulation environment. With this framework, we investigate how deep RL-based adaptive traffic controllers perform under different scenarios, namely under demand surges caused by special events, capacity reductions from incidents and sensor failures. We extract several key insights for the development of robust deep RL algorithms for traffic control and propose concrete designs to mitigate the impact of the considered exogenous uncertainties.