# A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles

arXiv: https://arxiv.org/abs/2111.00278 · HTML: https://mlsanity.com/paper/2111.00278/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 65 references that are in the map.
- First posted on arXiv 30 Oct 2021.

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

- [Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Control](https://mlsanity.com/paper/1903.04527/) (2019)
- [CoLight: Learning Network-level Cooperation for Traffic Signal Control](https://mlsanity.com/paper/1905.05717/) (2019)
- [Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1702.08887/) (2017)
- [Learning Phase Competition for Traffic Signal Control](https://mlsanity.com/paper/1905.04722/) (2019)
- [SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2108.06227/) (2021)
- [CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)](https://mlsanity.com/paper/1808.04256/) (2018)
- [A Survey on Traffic Signal Control Methods](https://mlsanity.com/paper/1904.08117/) (2019)
- [Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answering](https://mlsanity.com/paper/2109.03381/) (2021)
- [Towards Data Distillation for End-to-end Spoken Conversational Question Answering](https://mlsanity.com/paper/2010.08923/) (2020)
- [Knowledge Distillation for Improved Accuracy in Spoken Question Answering](https://mlsanity.com/paper/2010.11067/) (2020)
- [Unsupervised Wasserstein Distance Guided Domain Adaptation for 3D Multi-Domain Liver Segmentation](https://mlsanity.com/paper/2009.02831/) (2020)
- [Self-supervised Dialogue Learning for Spoken Conversational Question Answering](https://mlsanity.com/paper/2106.02182/) (2021)
- [Contextualized Attention-based Knowledge Transfer for Spoken Conversational Question Answering](https://mlsanity.com/paper/2010.11066/) (2020)
- [Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising](https://mlsanity.com/paper/1805.00587/) (2018)
- [Adaptive Bi-directional Attention: Exploring Multi-Granularity Representations for Machine Reading Comprehension](https://mlsanity.com/paper/2012.10877/) (2020)
- [Low-Dose CT via Deep CNN with Skip Connection and Network in Network](https://mlsanity.com/paper/1811.10564/) (2018)
- [MEGAN: Memory Enhanced Graph Attention Network for Space-Time Video Super-Resolution](https://mlsanity.com/paper/2110.15327/) (2021)
- [A Hybrid Queuing Model for Coordinated Vehicle Platooning on Mixed-Autonomy Highways: Training and Validation](https://mlsanity.com/paper/2103.14202/) (2021)

## Built on by (most cited first, 9 of 9)

- [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)
- [EMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System](https://mlsanity.com/paper/2206.13441/) (2022)
- [VLMLight: Safety-Critical Traffic Signal Control via Vision-Language Meta-Control and Dual-Branch Reasoning Architecture](https://mlsanity.com/paper/2505.19486/) (2025)
- [Model-free Learning of Corridor Clearance: A Near-term Deployment Perspective](https://mlsanity.com/paper/2312.10339/) (2023)
- [Configurable Mirror Descent: Towards a Unification of Decision Making](https://mlsanity.com/paper/2405.11746/) (2024)
- [Retrieval Augmented Generation-Enhanced Distributed LLM Agents for Generalizable Traffic Signal Control with Emergency Vehicles](https://mlsanity.com/paper/2510.26242/) (2025)
- [LightSim: A Lightweight Cell Transmission Model Simulator for Traffic Signal Control Research](https://mlsanity.com/paper/2602.21852/) (2026)
- [Emergency Preemption Without Online Exploration: A Decision Transformer Approach](https://mlsanity.com/paper/2603.22315/) (2026)
- [ReasonLight: A Multimodal Foundation Model-Enhanced Reinforcement Learning Framework for Zero-Shot Traffic Signal Control](https://mlsanity.com/paper/2605.29425/) (2026)

## Abstract (arXiv)

Emergency vehicles (EMVs) play a critical role in a city's response to time-critical events such as medical emergencies and fire outbreaks. The existing approaches to reduce EMV travel time employ route optimization and traffic signal pre-emption without accounting for the coupling between route these two subproblems. As a result, the planned route often becomes suboptimal. In addition, these approaches also do not focus on minimizing disruption to the overall traffic flow. To address these issues, we introduce EMVLight in this paper. This is a decentralized reinforcement learning (RL) framework for simultaneous dynamic routing and traffic signal control. EMVLight extends Dijkstra's algorithm to efficiently update the optimal route for an EMV in real-time as it travels through the traffic network. Consequently, the decentralized RL agents learn network-level cooperative traffic signal phase strategies that reduce EMV travel time and the average travel time of non-EMVs in the network. We have carried out comprehensive experiments with synthetic and real-world maps to demonstrate this benefit. Our results show that EMVLight outperforms benchmark transportation engineering techniques as well as existing RL-based traffic signal control methods.