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Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning

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.
  • 9. Oktober 2024

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- Rabat på køb af fysiske bøger
- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding

Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.

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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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