# DRLE: Decentralized Reinforcement Learning at the Edge for Traffic Light Control in the IoV

arXiv: https://arxiv.org/abs/2009.01502 · HTML: https://mlsanity.com/paper/2009.01502/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [MDPGT: Momentum-based Decentralized Policy Gradient Tracking](https://mlsanity.com/paper/2112.02813/) (2021)
- [MCMARL: Parameterizing Value Function via Mixture of Categorical Distributions for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2202.10134/) (2022)
- [Learning Decentralized Traffic Signal Controllers with Multi-Agent Graph Reinforcement Learning](https://mlsanity.com/paper/2311.03756/) (2023)
- [The Fusion of Deep Reinforcement Learning and Edge Computing for Real-time Monitoring and Control Optimization in IoT Environments](https://mlsanity.com/paper/2403.07923/) (2024)
- [Toward Dependency Dynamics in Multi-Agent Reinforcement Learning for Traffic Signal Control](https://mlsanity.com/paper/2502.16608/) (2025)
- [Topology-Aware Knowledge Propagation in Decentralized Learning](https://mlsanity.com/paper/2505.11760/) (2025)
- [A Robust and Efficient Multi-Agent Reinforcement Learning Framework for Traffic Signal Control](https://mlsanity.com/paper/2603.12096/) (2026)

## Abstract (arXiv)

The Internet of Vehicles (IoV) enables real-time data exchange among vehicles and roadside units and thus provides a promising solution to alleviate traffic jams in the urban area. Meanwhile, better traffic management via efficient traffic light control can benefit the IoV as well by enabling a better communication environment and decreasing the network load. As such, IoV and efficient traffic light control can formulate a virtuous cycle. Edge computing, an emerging technology to provide low-latency computation capabilities at the edge of the network, can further improve the performance of this cycle. However, while the collected information is valuable, an efficient solution for better utilization and faster feedback has yet to be developed for edge-empowered IoV. To this end, we propose a Decentralized Reinforcement Learning at the Edge for traffic light control in the IoV (DRLE). DRLE exploits the ubiquity of the IoV to accelerate the collection of traffic data and its interpretation towards alleviating congestion and providing better traffic light control. DRLE operates within the coverage of the edge servers and uses aggregated data from neighboring edge servers to provide city-scale traffic light control. DRLE decomposes the highly complex problem of large area control. into a decentralized multi-agent problem. We prove its global optima with concrete mathematical reasoning. The proposed decentralized reinforcement learning algorithm running at each edge node adapts the traffic lights in real time. We conduct extensive evaluations and demonstrate the superiority of this approach over several state-of-the-art algorithms.