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Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems|Tatiana Tatarenko

Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems

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Overview

Presents new, efficient methods for optimization in large-scale multi-agent systems
Develops efficient optimization algorithms for three different information settings in multi-agent systems
Sets optimization problems without common restrictive assumptions

This item is Non-Returnable

Details

  • ISBN-13: 9783319654782
  • ISBN-10: 3319654780
  • Publisher: Springer
  • Publish Date: September 2017
  • Dimensions: 9.21 x 6.14 x 0.5 inches
  • Shipping Weight: 0.96 pounds
  • Page Count: 171

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