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Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning - Alborz Geramifard - Bog

af Alborz Geramifard, Thomas J. Walsh, Girish Chowdhary, Nicholas Roy, Jonathan P. How, Tellex Stefanie

indgår i Foundations and Trends (R) in Machine Learning serien

Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning af Alborz Geramifard
Bag om Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning

A Markov Decision Process (MDP) is a natural framework for formulating sequential decision-making problems under uncertainty. In recent years, researchers have greatly advanced algorithms for learning and acting in MDPs. This book reviews such algorithms.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9781601987600
  • Indbinding:
  • Paperback
  • Sideantal:
  • 92
  • Udgivet:
  • 19. December 2013
  • Størrelse:
  • 234x156x5 mm.
  • Vægt:
  • 143 g.
  • 2-3 uger.
  • 23. Juli 2024

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Medlemspris

Prøv i 30 dage for 45 kr.
Herefter fra 79 kr./md. Ingen binding.

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Beskrivelse af Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning

A Markov Decision Process (MDP) is a natural framework for formulating sequential decision-making problems under uncertainty. In recent years, researchers have greatly advanced algorithms for learning and acting in MDPs. This book reviews such algorithms.

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