Coverart for item
The Resource Game-theoretic learning and distributed optimization in memoryless multi-agent systems, Tatiana Tatarenko, (electronic resource)

Game-theoretic learning and distributed optimization in memoryless multi-agent systems, Tatiana Tatarenko, (electronic resource)

Label
Game-theoretic learning and distributed optimization in memoryless multi-agent systems
Title
Game-theoretic learning and distributed optimization in memoryless multi-agent systems
Statement of responsibility
Tatiana Tatarenko
Creator
Subject
Language
eng
Member of
Cataloging source
YDX
http://library.link/vocab/creatorName
Tatarenko, Tatiana
Dewey number
  • 006.3/1
  • 510
Index
no index present
LC call number
Q325.5
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/subjectName
  • Machine learning
  • Multiagent systems
  • Game theory
Label
Game-theoretic learning and distributed optimization in memoryless multi-agent systems, Tatiana Tatarenko, (electronic resource)
Instantiates
Publication
Bibliography note
Includes bibliographical references
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Abstract; Contents; 1 Introduction; 1.1 Motivation of Research; 1.2 List of Notations; 2 Game Theory and Multi-Agent Optimization; 2.1 Game Theory; 2.1.1 Introduction to Game Theory; 2.1.2 Nash Equilibrium; 2.1.3 Potential Games; 2.2 Potential Game Design in Multi-Agent Optimization; 2.2.1 Multi-Agent Systems Modeled by Means of Potential Games; 2.2.2 Learning Optimal States in Potential Games; 2.3 Distributed Optimization in Multi-Agent Systems; References; 3 Logit Dynamics in Potential Games with Memoryless Players; 3.1 Introduction
  • 3.2 Memoryless Learning in Discrete Action Games as a Regular Perturbed Markov Chain3.2.1 Preliminaries: Regular Perturbed Markov Chains; 3.2.2 Convergence in Total Variation of General Memoryless Learning Algorithms; 3.3 Asynchronous Learning; 3.3.1 Log-Linear Learning in Discrete Action Games; 3.3.1.1 An Example: Log-Linear Learning for Consensus Problem; 3.3.2 Convergence to Potential Function Maximizers; 3.4 Synchronization in Memoryless Learning; 3.4.1 Additional Information is Needed; 3.4.2 Independent Log-Linear Learning in Discrete Action Games
  • 3.4.3 Convergence to Potential Function Maximizers3.5 Convergence Rate Estimation and Finite Time Behavior; 3.5.1 Convergence Rate of Time-Inhomogeneous Log-Linear Learning; 3.5.2 Convergence Rate of Time-Inhomogeneous Independent Log-Linear Learning; 3.5.3 Simulation Results: Example of a SensorCoverage Problem; 3.5.3.1 Inhomogeneous Log-Linear Learning in Coverage Problem; 3.5.3.2 Inhomogeneous Independent Log-Linear Learning in Coverage Problem; 3.6 Learning in Continuous Action Games; 3.6.1 Log-Linear Learning in Continuous Action Games
  • 4.3 Push-Sum Algorithm in Non-convex Distributed Optimization4.3.1 Problem Formulation: Push-Sum Algorithm and Assumptions; 4.3.2 Convergence to Critical Points; 4.3.3 Perturbed Procedure: Convergence to Local Minima; 4.3.4 Convergence Rate of the Perturbed Process; 4.3.5 Simulation Results: Illustrative Example and Congestion Routing Problem; 4.4 Communication-Based Memoryless Learningin Potential Games; 4.4.1 Simulation Results: Code Division Multiple Access Problem; 4.5 Payoff-Based Learning in Potential Games; 4.5.1 Convergence to a Local Maximum of the PotentialFunction
Dimensions
unknown
Extent
1 online resource.
Form of item
online
Isbn
9783319654782
Media category
computer
Media MARC source
rdamedia
Media type code
c
Specific material designation
remote
System control number
on1004763612
Label
Game-theoretic learning and distributed optimization in memoryless multi-agent systems, Tatiana Tatarenko, (electronic resource)
Publication
Bibliography note
Includes bibliographical references
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Abstract; Contents; 1 Introduction; 1.1 Motivation of Research; 1.2 List of Notations; 2 Game Theory and Multi-Agent Optimization; 2.1 Game Theory; 2.1.1 Introduction to Game Theory; 2.1.2 Nash Equilibrium; 2.1.3 Potential Games; 2.2 Potential Game Design in Multi-Agent Optimization; 2.2.1 Multi-Agent Systems Modeled by Means of Potential Games; 2.2.2 Learning Optimal States in Potential Games; 2.3 Distributed Optimization in Multi-Agent Systems; References; 3 Logit Dynamics in Potential Games with Memoryless Players; 3.1 Introduction
  • 3.2 Memoryless Learning in Discrete Action Games as a Regular Perturbed Markov Chain3.2.1 Preliminaries: Regular Perturbed Markov Chains; 3.2.2 Convergence in Total Variation of General Memoryless Learning Algorithms; 3.3 Asynchronous Learning; 3.3.1 Log-Linear Learning in Discrete Action Games; 3.3.1.1 An Example: Log-Linear Learning for Consensus Problem; 3.3.2 Convergence to Potential Function Maximizers; 3.4 Synchronization in Memoryless Learning; 3.4.1 Additional Information is Needed; 3.4.2 Independent Log-Linear Learning in Discrete Action Games
  • 3.4.3 Convergence to Potential Function Maximizers3.5 Convergence Rate Estimation and Finite Time Behavior; 3.5.1 Convergence Rate of Time-Inhomogeneous Log-Linear Learning; 3.5.2 Convergence Rate of Time-Inhomogeneous Independent Log-Linear Learning; 3.5.3 Simulation Results: Example of a SensorCoverage Problem; 3.5.3.1 Inhomogeneous Log-Linear Learning in Coverage Problem; 3.5.3.2 Inhomogeneous Independent Log-Linear Learning in Coverage Problem; 3.6 Learning in Continuous Action Games; 3.6.1 Log-Linear Learning in Continuous Action Games
  • 4.3 Push-Sum Algorithm in Non-convex Distributed Optimization4.3.1 Problem Formulation: Push-Sum Algorithm and Assumptions; 4.3.2 Convergence to Critical Points; 4.3.3 Perturbed Procedure: Convergence to Local Minima; 4.3.4 Convergence Rate of the Perturbed Process; 4.3.5 Simulation Results: Illustrative Example and Congestion Routing Problem; 4.4 Communication-Based Memoryless Learningin Potential Games; 4.4.1 Simulation Results: Code Division Multiple Access Problem; 4.5 Payoff-Based Learning in Potential Games; 4.5.1 Convergence to a Local Maximum of the PotentialFunction
Dimensions
unknown
Extent
1 online resource.
Form of item
online
Isbn
9783319654782
Media category
computer
Media MARC source
rdamedia
Media type code
c
Specific material designation
remote
System control number
on1004763612

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