[go: up one dir, main page]

Xu et al., 2020 - Google Patents

Distributed no-regret learning in multiagent systems: Challenges and recent developments

Xu et al., 2020

View PDF
Document ID
6727087175838462700
Author
Xu X
Zhao Q
Publication year
Publication venue
IEEE Signal Processing Magazine

External Links

Snippet

Game theory is a well-established tool for studying interactions among self-interested players. Under the assumption of complete information on the game composition at each player, the focal point of game-theoretic studies has been on the Nash equilibrium (NE) in …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

    • 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
    • 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
    • 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
    • G06N7/00Computer systems based on specific mathematical models
    • G06N7/005Probabilistic networks
    • 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
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • 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"
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00Data switching networks
    • H04L12/02Details
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00Arrangements for maintenance or administration or management of packet switching networks
    • H04L41/14Arrangements for maintenance or administration or management of packet switching networks involving network analysis or design, e.g. simulation, network model or planning
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only

Similar Documents

Publication Publication Date Title
Jin et al. Reward-free exploration for reinforcement learning
Lieder et al. An automatic method for discovering rational heuristics for risky choice
Xu et al. Distributed no-regret learning in multiagent systems: Challenges and recent developments
Lee et al. An intelligent fuzzy agent for meeting scheduling decision support system
CN110945542A (en) A multi-agent deep reinforcement learning agent method based on smart grid
Bighashdel et al. Policy space response oracles: A survey
Gummadi et al. Mean field analysis of multi-armed bandit games
Farhan et al. Reinforcement learning in anylogic simulation models: a guiding example using pathmind
Wu et al. Adaptive QoE-aware SFC orchestration in UAV networks: A deep reinforcement learning approach
Dao et al. Compact artificial bee colony
Gyeera et al. Regression analysis of predictions and forecasts of cloud data center KPIs using the boosted decision tree algorithm
Rishwaraj et al. Heuristics-based trust estimation in multiagent systems using temporal difference learning
Deng et al. Algorithmic collusion in dynamic pricing with deep reinforcement learning
Gattami et al. Reinforcement learning for multi-objective and constrained Markov decision processes
Sadoune et al. Algorithmic collusion and the minimum price markov game
Reddy et al. Negotiated learning for smart grid agents: entity selection based on dynamic partially observable features
Basaklar et al. GEM-RL: Generalized energy management of wearable devices using reinforcement learning
Mozo et al. Scalable prediction of service-level events in datacenter infrastructure using deep neural networks
Wan et al. Scheduling real-time wireless traffic: A network-aided offline reinforcement learning approach
CN109543879A (en) Load forecasting method and device neural network based
Kasumba et al. Data-driven goal recognition design for general behavioral agents
Kaliappan et al. Optimizing resource allocation in healthcare systems for efficient pandemic management using machine learning and artificial neural networks
CN116957053A (en) Sequential decision method, device and equipment based on dual cyclic neural network
US20230122472A1 (en) Hybrid Techniques for Quality Estimation of a Decision-Making Policy in a Computer System
Hegde et al. COUNSEL: Cloud resource configuration management using deep reinforcement learning