Multi-Agent Machine Learning: A Reinforcement Approach Front Cover

Multi-Agent Machine Learning: A Reinforcement Approach

  • Length: 256 pages
  • Edition: 1
  • Publisher:
  • Publication Date: 2014-08-11
  • ISBN-10: 111836208X
  • ISBN-13: 9781118362082
  • Sales Rank: #996292 (See Top 100 Books)
Description

Multi-Agent Machine Learning: A Reinforcement Learning Approach is a framework to understanding different methods and approaches in multi-agent machine learning. It also provides cohesive coverage of the latest advances in multi-agent differential games and presents applications in game theory and robotics.

  • Framework for understanding a variety of methods and approaches in multi-agent machine learning.
  • Discusses methods of reinforcement learning such as a number of forms of multi-agent Q-learning
  • Applicable to research professors and graduate students studying electrical and computer engineering, computer science, and mechanical and aerospace engineering

Table of Contents

Chapter 1: A Brief Review of Supervised Learning
Chapter 2: Single-Agent Reinforcement Learning
Chapter 3: Learning in Two-Player Matrix Games
Chapter 4: Learning in Multiplayer Stochastic Games
Chapter 5: Differential Games
Chapter 6: Swarm Intelligence and the Evolution of Personality Traits

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