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Traffic Signal Control

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Control traffic lights/signals to optimize traffic.

<span style="color:grey; opacity: 0.6">( Image credit: Flaticon )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Aegis1863/MacLight

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MacLight: Multi-scene Aggregation Convolutional Learning for Traffic Signal Control

https://github.com/DaRL-LibSignal/LibSignal

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LibSignal: An Open Library for Traffic Signal Control

https://github.com/FXDevailly/IG-RL

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IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control

https://github.com/LiangZhang1996/Advanced_XLight

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Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control

https://github.com/LiangZhang1996/DataLight

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DataLight: Offline Data-Driven Traffic Signal Control

https://github.com/LiangZhang1996/QL_XLight

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Leveraging Queue Length and Attention Mechanisms for Enhanced Traffic Signal Control Optimization

https://github.com/LucasAlegre/sumo-rl

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A Comparative Study of Algorithms for Intelligent Traffic Signal Control

https://github.com/Rabbytr/g2p-tsc

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Generalized Phase Pressure Control Enhanced Reinforcement Learning for Traffic Signal Control

https://github.com/Skylark0924/Gamma_Reward

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Learning Scalable Multi-Agent Coordination by Spatial Differentiation for Traffic Signal Control

https://github.com/Traffic-Alpha/CCDA-Light

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Traffic Signal Cycle Control with Centralized Critic and Decentralized Actors under Varying Intervention Frequencies

https://github.com/amsterdam-internships/self-learning-traffic-lights

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Back to Basics: Deep Reinforcement Learning in Traffic Signal Control

https://github.com/anonymousaccountss/coslight

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CoSLight: Co-optimizing Collaborator Selection and Decision-making to Enhance Traffic Signal Control

https://github.com/anonymousidforsubmission/guidedlight

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GuideLight: "Industrial Solution" Guidance for More Practical Traffic Signal Control Agents

https://github.com/asatheesh6561/mappo-lce

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A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal Control

https://github.com/badger-rl/stop

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Learning to Stabilize Online Reinforcement Learning in Unbounded State Spaces

https://github.com/cityflow-project/CityFlow

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CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario

https://github.com/cjwyx/ph-ddpg-model

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A Parallel Hybrid Action Space Reinforcement Learning Model for Real-world Adaptive Traffic Signal Control

https://github.com/cts198859/deeprl_network

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Multi-agent Reinforcement Learning for Networked System Control

https://github.com/cts198859/deeprl_signal_control

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Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Control

https://github.com/darl-libsignal/openti

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Open-TI: Open Traffic Intelligence with Augmented Language Model

https://github.com/darl-libsignal/promptgat

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Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning

https://github.com/darl-libsignal/syntrac

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SynTraC: A Synthetic Dataset for Traffic Signal Control from Traffic Monitoring Cameras

https://github.com/darl-libsignal/ugat

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Uncertainty-aware Grounded Action Transformation towards Sim-to-Real Transfer for Traffic Signal Control

https://github.com/daweidavidwang/mixedtrafficcontrol

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Learning to Control and Coordinate Mixed Traffic Through Robot Vehicles at Complex and Unsignalized Intersections

https://github.com/decenter2021/saffron

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Feedback-feedforward Signal Control with Exogenous Demand Estimation in Congested Urban Road Networks

https://github.com/derekmei233/MissLight

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Reinforcement Learning Approaches for Traffic Signal Control under Missing Data

https://github.com/dimvlachogiannis/humanlight

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HumanLight: Incentivizing Ridesharing via Human-centric Deep Reinforcement Learning in Traffic Signal Control

https://github.com/docwza/sumolights

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An Open-Source Framework for Adaptive Traffic Signal Control

https://github.com/edun-eyes/tsc

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Towards Multi-agent Reinforcement Learning based Traffic Signal Control through Spatio-temporal Hypergraphs

https://github.com/fmpr/CAREL

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

https://github.com/haozhougt/signal_control_paper

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Congested Urban Networks Tend to Be Insensitive to Signal Settings: Implications for Learning-Based Control

https://github.com/jault/StateStreetSumo

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Learning an Interpretable Traffic Signal Control Policy

https://github.com/jianghaoyuan1994/x-light

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X-Light: Cross-City Traffic Signal Control Using Transformer on Transformer as Meta Multi-Agent Reinforcement Learner

https://github.com/junfanlin/denselight

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DenseLight: Efficient Control for Large-scale Traffic Signals with Dense Feedback

https://github.com/liangzhang1996/attentionlight

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Leveraging Queue Length and Attention Mechanisms for Enhanced Traffic Signal Control Optimization

https://github.com/liangzhang1996/dynamiclight

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DynamicLight: Two-Stage Dynamic Traffic Signal Timing

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