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A Universal Offline Reinforcement Learning Model for Adaptive Traffic Signal Control at Heterogeneous Intersections

https://doi.org/10.2139/ssrn.4760719
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.engappai.2014.01.007openalex-extracted
Adaptive multi-objective reinforcement learning with hybrid exploration for traffic signal control based on cooperative multi-agent framework
inferredcites →https://doi.org/10.1016/j.knosys.2022.109166openalex-extracted
Meta-learning based spatial-temporal graph attention network for traffic signal control
inferredcites →https://doi.org/10.1016/j.neunet.2021.03.015openalex-extracted
IHG-MA: Inductive heterogeneous graph multi-agent reinforcement learning for multi-intersection traffic signal control
inferredcites →https://doi.org/10.1016/j.trc.2013.08.014openalex-extracted
Max pressure control of a network of signalized intersections
inferredcites →https://doi.org/10.1016/j.trc.2021.103046openalex-extracted
Adaptive Traffic Signal Control for large-scale scenario with Cooperative Group-based Multi-agent reinforcement learning
inferredcites →https://doi.org/10.1016/j.trc.2021.103321openalex-extracted
Transferable traffic signal control: Reinforcement learning with graph centric state representation
inferredcites →https://doi.org/10.1016/s0925-2312(00)00324-6openalex-extracted
Reinforcement Learning: An Introduction
inferredcites →https://doi.org/10.1038/nature14236openalex-extracted
Human-level control through deep reinforcement learning
inferredcites →https://doi.org/10.1049/iet-its.2018.5170openalex-extracted
Value‐based deep reinforcement learning for adaptive isolated intersection signal control
inferredcites →https://doi.org/10.1109/icdmw.2018.00088openalex-extracted
Deep Reinforcement Learning for Traffic Light Optimization
inferredcites →https://doi.org/10.1109/itsc.2018.8569549openalex-extracted
Deep Learning vs. Discrete Reinforcement Learning for Adaptive Traffic Signal Control
inferredcites →https://doi.org/10.1109/itsc.2018.8569938openalex-extracted
Microscopic Traffic Simulation using SUMO
inferredcites →https://doi.org/10.1109/itsc48978.2021.9564683openalex-extracted
A Collaborative Communication-Qmix Approach for Large-scale Networked Traffic Signal Control
inferredcites →https://doi.org/10.1109/ojits.2026.3654547openalex-extracted
Traffic Signal Control Using Lightweight Transformers: An Offline-to-Online RL Approach
inferredcites →https://doi.org/10.1109/smc42975.2020.9283498openalex-extracted
Assessment of Reward Functions for Reinforcement Learning Traffic Signal Control under Real-World Limitations
inferredcites →https://doi.org/10.1109/t-vt.1980.23833openalex-extracted
The Sydney coordinated adaptive traffic (SCAT) system philosophy and benefits
inferredcites →https://doi.org/10.1109/tits.2021.3070835openalex-extracted
IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control
inferredcites →https://doi.org/10.1109/tits.2023.3308594openalex-extracted
Learning to Coordinate Traffic Signals With Adaptive Network Partition
inferredcites →https://doi.org/10.1109/tvt.2018.2890726openalex-extracted
A Deep Reinforcement Learning Network for Traffic Light Cycle Control
inferredcites →https://doi.org/10.1145/3219819.3220096openalex-extracted
IntelliLight
inferredcites →https://doi.org/10.1145/3292500.3330949openalex-extracted
PressLight
inferredcites →https://doi.org/10.1145/3308558.3314139openalex-extracted
CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario
inferredcites →https://doi.org/10.1145/3340531.3411859openalex-extracted
GeneraLight: Improving Environment Generalization of Traffic Signal Control via Meta Reinforcement Learning
inferredcites →https://doi.org/10.1145/3357384.3357902openalex-extracted
CoLight
inferredcites →https://doi.org/10.1145/3447556.3447565openalex-extracted
Recent Advances in Reinforcement Learning for Traffic Signal Control
inferredcites →https://doi.org/10.1145/3459637.3482254openalex-extracted
DynSTGAT
inferredcites →https://doi.org/10.1145/3580305.3599459openalex-extracted
Optimizing Traffic Control with Model-Based Learning: A Pessimistic Approach to Data-Efficient Policy Inference
inferredcites →https://doi.org/10.1609/aaai.v30i1.10295openalex-extracted
Deep Reinforcement Learning with Double Q-Learning
inferredcites →https://doi.org/10.1609/aaai.v34i04.5744openalex-extracted
Toward A Thousand Lights: Decentralized Deep Reinforcement Learning for Large-Scale Traffic Signal Control
inferredcites →https://doi.org/10.17863/cam.48429openalex-extracted
Graph Attention Networks
inferredcites →https://doi.org/10.3390/s21072302openalex-extracted
Traffic Signal Control Using Hybrid Action Space Deep Reinforcement Learning
inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-extracted
Adam: A Method for Stochastic Optimization
inferredcites →https://doi.org/10.48550/arxiv.1705.02755openalex-extracted
Adaptive Traffic Signal Control: Deep Reinforcement Learning Algorithm with Experience Replay and Target Network
inferredcites →https://doi.org/10.48550/arxiv.1801.01290openalex-extracted
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
inferredcites →https://doi.org/10.48550/arxiv.1905.04716openalex-extracted
Diagnosing Reinforcement Learning for Traffic Signal Control
inferredcites →https://doi.org/10.48550/arxiv.1906.00949openalex-extracted
Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction
inferredcites →https://doi.org/10.48550/arxiv.2005.01643openalex-extracted
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
inferredcites →https://doi.org/10.48550/arxiv.2006.04779openalex-extracted
Conservative Q-Learning for Offline Reinforcement Learning
inferredcites →https://doi.org/10.48550/arxiv.2010.05772openalex-extracted
AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control
inferredcites →https://doi.org/10.48550/arxiv.2106.01345openalex-extracted
Decision Transformer: Reinforcement Learning via Sequence Modeling
inferredcites →https://doi.org/10.52202/068431-1795openalex-extracted
NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning
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