Nothing Special   »   [go: up one dir, main page]

Mousavi et al., 2017 - Google Patents

Traffic light control using deep policy‐gradient and value‐function‐based reinforcement learning

Mousavi et al., 2017

View PDF @Full View
Document ID
15577450001624371510
Author
Mousavi S
Schukat M
Howley E
Publication year
Publication venue
IET Intelligent Transport Systems

External Links

Snippet

Recent advances in combining deep neural network architectures with reinforcement learning (RL) techniques have shown promising potential results in solving complex control problems with high‐dimensional state and action spaces. Inspired by these successes, in …
Continue reading at ietresearch.onlinelibrary.wiley.com (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/04Architectures, e.g. interconnection topology
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/08Learning methods
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/04Inference methods or devices
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50Computer-aided design
    • G06F17/5009Computer-aided design using simulation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/12Computer systems based on biological models using genetic models
    • G06N3/126Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/02Knowledge representation
    • G06N5/022Knowledge engineering, knowledge acquisition
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00Computer systems based on specific mathematical models
    • G06N7/005Probabilistic networks
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/04Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"

Similar Documents

Publication Publication Date Title
Mousavi et al. Traffic light control using deep policy‐gradient and value‐function‐based reinforcement learning
Wang et al. Deep reinforcement learning for transportation network combinatorial optimization: A survey
Cai et al. Proxylessnas: Direct neural architecture search on target task and hardware
Tong et al. Directed graph contrastive learning
Wang et al. Interpretable decision-making for autonomous vehicles at highway on-ramps with latent space reinforcement learning
CN113362491B (en) Vehicle track prediction and driving behavior analysis method
Wei et al. Learning motion rules from real data: Neural network for crowd simulation
Balhara et al. A survey on deep reinforcement learning architectures, applications and emerging trends
Genders et al. Policy analysis of adaptive traffic signal control using reinforcement learning
CN113326884B (en) Efficient learning method and device for large-scale heterograph node representation
Liu et al. Smart city moving target tracking algorithm based on quantum genetic and particle filter
Wang et al. TransWorldNG: Traffic simulation via foundation model
Liu et al. Graph convolution-based deep reinforcement learning for multi-agent decision-making in interactive traffic scenarios
Kumar et al. Adaptive traffic light control using deep reinforcement learning technique
Huang et al. Improving traffic signal control operations using proximal policy optimization
Jiang et al. A general scenario-agnostic reinforcement learning for traffic signal control
Quek et al. Deep Q‐network implementation for simulated autonomous vehicle control
Xu et al. Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Survey
Chen et al. Deep Q-learning with hybrid quantum neural network on solving maze problems
Zhang et al. A Survey of Generative Techniques for Spatial-Temporal Data Mining
Liu et al. Graph convolution-based deep reinforcement learning for multi-agent decision-making in mixed traffic environments
Hu et al. Dynamic traffic signal control using mean field multi‐agent reinforcement learning in large scale road‐networks
US20230289563A1 (en) Multi-node neural network constructed from pre-trained small networks
CN116484016B (en) Time sequence knowledge graph reasoning method and system based on automatic maintenance of time sequence path
Gora et al. Investigating performance of neural networks and gradient boosting models approximating microscopic traffic simulations in traffic optimization tasks