Multi-agent machine learning

Multi-agent machine learning: a reinforcement approach

by Schwartz, Howard M.

Type Book
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"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-learn…

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Title Statements

  • Full title :Multi-agent machine learning : a reinforcement approach / Howard M. Schwartz.

Summary

"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."

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Edition Statements

  • Publication Date:2014.

Publication Statements

  • Publisher(s):Wiley
  • Place of Publication:Hoboken, NJ
  • Language:eng
  • Publication Date:2014.

The Physical Object

  • Description:xi, 242 Seiten : : Diagramme

Edition Identifiers

  • ISBN:9781118362082, 111836208X
  • oclc:ocn897814171
  • LC Classification:Q325.6
  • Dewey Decimal:519.3

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