Large-Scale Traffic Signal Control Using Constrained Network Partition and Adaptive Deep Reinforcement Learning
Multi-agent Deep Reinforcement Learning (MADRL) based traffic signal control becomes a popular research topic in recent years. To alleviate the scalability issue of completely centralized RL techniques and the non-stationarity issue of completely decentralized RL techniques on large-scale traffic networks, some literature utilizes a regional control approach where the whole network is firstly partitioned into multiple disjoint regions, followed by applying the centralized RL approach to each region. However, the existing partitioning rules either have no constraints on the topology of regions or require the same topology for all regions. Meanwhile, no existing regional control approach explores the performance of optimal joint action in an exponentially growing regional action space when intersections are controlled by 4-phase traffic signals (EW, EWL, NS, NSL). In this paper, we propose a novel RL training framework named RegionLight to tackle the above limitations. Specifically, the topology of regions is firstly constrained to a star network which comprises one center and an arbitrary number of leaves. Next, the network partitioning problem is modeled as an optimization problem to minimize the number of regions. Then, an Adaptive Branching Dueling Q-Network (ABDQ) model is proposed to decompose the regional control task into several joint signal control sub-tasks corresponding to particular intersections. Subsequently, these sub-tasks maximize the regional benefits cooperatively. Finally, the global control strategy for the whole network is obtained by concatenating the optimal joint actions of all regions. Experimental results demonstrate the superiority of our proposed framework over all baselines under both real and synthetic datasets in all evaluation metrics.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep Reinforcement LearningMulti-agent Reinforcement Learningreinforcement-learningTraffic Signal ControlMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
LLM-Augmented Traffic Signal Control with LSTM-Based Traffic State Prediction and Safety-Constrained Decision Support
Traffic signal control is a critical task in intelligent transportation systems, yet conventional fixed-time and rule-based methods often struggle to adapt to dynamic traffic demand and provide limited decision interpret…
Large-Scale Mixed-Traffic and Intersection Control using Multi-agent Reinforcement Learning
Traffic congestion remains a significant challenge in modern urban networks. Autonomous driving technologies have emerged as a potential solution. Among traffic control methods, reinforcement learning has shown superior …
Autonomous DrivingMulti-agent Reinforcement Learningreinforcement-learningReinforcement LearningA Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal Control
Traffic congestion in modern cities is exacerbated by the limitations of traditional fixed-time traffic signal systems, which fail to adapt to dynamic traffic patterns. Adaptive Traffic Signal Control (ATSC) algorithms h…
FairnessMulti-agent Reinforcement LearningTraffic Signal ControlModel-based graph reinforcement learning for inductive traffic signal control
Most reinforcement learning methods for adaptive-traffic-signal-control require training from scratch to be applied on any new intersection or after any modification to the road network, traffic distribution, or behavior…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Traffic Signal ControlUsing a Deep Reinforcement Learning Agent for Traffic Signal Control
Ensuring transportation systems are efficient is a priority for modern society. Technological advances have made it possible for transportation systems to collect large volumes of varied data on an unprecedented scale. W…
Deep Reinforcement LearningQ-Learningreinforcement-learningReinforcement Learning+2