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Deep Reinforcement Meta-Learning and Self-Organization in Complex Systems: Applications to Traffic Signal Control
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-319-25808-9_4openalex-snapshot
An Experimental Review of Reinforcement Learning Algorithms for Adaptive Traffic Signal Control
inferredcites →https://doi.org/10.1007/978-3-540-39432-7_65openalex-snapshot
When Can We Call a System Self-Organizing?
inferredcites →https://doi.org/10.1016/j.jcp.2018.10.045openalex-snapshot
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
inferredcites →https://doi.org/10.1038/s41467-021-27590-0openalex-snapshot
AI Pontryagin or how artificial neural networks learn to control dynamical systems
inferredcites →https://doi.org/10.1038/s41598-022-21125-3openalex-snapshot
Adaptability and sustainability of machine learning approaches to traffic signal control
inferredcites →https://doi.org/10.1088/1742-5468/2008/04/p04019openalex-snapshot
Self-control of traffic lights and vehicle flows in urban road networks
inferredcites →https://doi.org/10.1093/comnet/cnz018openalex-snapshot
Deep learning systems as complex networks
inferredcites →https://doi.org/10.1103/revmodphys.73.1067openalex-snapshot
Traffic and related self-driven many-particle systems
inferredcites →https://doi.org/10.1109/access.2022.3204057openalex-snapshot
Analytically Guided Reinforcement Learning for Green It and Fluent Traffic
inferredcites →https://doi.org/10.1109/access.2023.3266644openalex-snapshot
How Well Do Reinforcement Learning Approaches Cope With Disruptions? The Case of Traffic Signal Control
inferredcites →https://doi.org/10.1109/dasc-picom-cbdcom-cyberscitech52372.2021.00019openalex-snapshot
A Meta Multi-agent Reinforcement Learning Algorithm for Multi-intersection Traffic Signal Control
inferredcites →https://doi.org/10.1109/robot.2010.5509621openalex-snapshot
Superhuman performance of surgical tasks by robots using iterative learning from human-guided demonstrations
inferredcites →https://doi.org/10.1126/science.aar6404openalex-snapshot
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
inferredcites →https://doi.org/10.1145/3340531.3411859openalex-snapshot
GeneraLight: Improving Environment Generalization of Traffic Signal Control via Meta Reinforcement Learning
inferredcites →https://doi.org/10.1145/3457607openalex-snapshot
A Survey on Bias and Fairness in Machine Learning
inferredcites →https://doi.org/10.1177/26339137221114874openalex-snapshot
Collective intelligence for deep learning: A survey of recent developments
inferredcites →https://doi.org/10.1609/aaai.v34i01.5467openalex-snapshot
MetaLight: Value-Based Meta-Reinforcement Learning for Traffic Signal Control
inferredcites →https://doi.org/10.48550/arxiv.1703.03400openalex-snapshot
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4598461openalex-extracted
Democratizing Traffic Control in Smart Cities
inferred← citeshttps://doi.org/10.1016/j.trc.2024.104511openalex-snapshot
Democratizing traffic control in smart cities
inferred← citeshttps://doi.org/10.1016/j.trip.2025.101618openalex-snapshot
Mycomobility: Analysis of human transport through a mycorrhizal analogy
inferred← citeshttps://doi.org/10.1063/5.0195208openalex-snapshot
Model predictive complex system control from observational and interventional data
inferred← citeshttps://doi.org/10.1109/idciot59759.2024.10467630openalex-snapshot
Deep Adaptive Algorithms for Local Urban Traffic Control: Deep Reinforcement Learning with DQN
inferred← citeshttps://doi.org/10.3390/biology14030288openalex-snapshot
Enhancing Single-Cell and Bulk Hi-C Data Using a Generative Transformer Model
inferred← citeshttps://doi.org/10.3390/e27111169openalex-snapshot
Unveiling Self-Organization and Emergent Phenomena in Urban Transportation Systems via Multilayer Network Analysis
inferred← citeshttps://doi.org/10.3390/math12233768openalex-snapshot
Analyzing Decision-Making in Cognitive Agent Simulations Using Generalized Linear Mixed-Effects Models
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