DRLE: Decentralized Reinforcement Learning at the Edge for Traffic Light Control in the IoV
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.
Code (1)
Tasks
Edge-computingManagementMathematical Reasoningreinforcement-learningReinforcement Learning (RL)Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Reinforced Contact Tracing and Epidemic Intervention
The recent outbreak of COVID-19 poses a serious threat to people's lives. Epidemic control strategies have also caused damage to the economy by cutting off humans' daily commute. In this paper, we develop an Individual-b…
EMVLight: A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles
Emergency vehicles (EMVs) play a crucial role in responding to time-critical events such as medical emergencies and fire outbreaks in an urban area. The less time EMVs spend traveling through the traffic, the more likely…
reinforcement-learningReinforcement Learning (RL)Traffic Signal ControlA Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles
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 …
reinforcement-learningReinforcement Learning (RL)Traffic Signal ControlEMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System
Emergency vehicles (EMVs) play a crucial role in responding to time-critical calls such as medical emergencies and fire outbreaks in urban areas. Existing methods for EMV dispatch typically optimize routes based on histo…
Multi-agent Reinforcement LearningReinforcement Learning (RL)Traffic Signal ControlLearning Decentralized Traffic Signal Controllers with Multi-Agent Graph Reinforcement Learning
This paper considers optimal traffic signal control in smart cities, which has been taken as a complex networked system control problem. Given the interacting dynamics among traffic lights and road networks, attaining co…
Graph LearningMulti-agent Reinforcement Learningreinforcement-learningTraffic Signal Control