Learning Representation and Control in Markov Decision Processes

Learning Representation and Control in Markov Decision Processes
Author: Sridhar Mahadevan
Publisher: Now Publishers Inc
Total Pages: 185
Release: 2009
Genre: Computers
ISBN: 1601982380


Download Learning Representation and Control in Markov Decision Processes Book in PDF, Epub and Kindle

Provides a comprehensive survey of techniques to automatically construct basis functions or features for value function approximation in Markov decision processes and reinforcement learning.


Learning Representation and Control in Markov Decision Processes
Language: en
Pages: 185
Authors: Sridhar Mahadevan
Categories: Computers
Type: BOOK - Published: 2009 - Publisher: Now Publishers Inc

GET EBOOK

Provides a comprehensive survey of techniques to automatically construct basis functions or features for value function approximation in Markov decision process
Markov Decision Processes in Artificial Intelligence
Language: en
Pages: 367
Authors: Olivier Sigaud
Categories: Technology & Engineering
Type: BOOK - Published: 2013-03-04 - Publisher: John Wiley & Sons

GET EBOOK

Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under uncertainty as well as reinforcement learning prob
Hierarchical Control and Learning for Markov Decision Processes
Language: en
Pages: 346
Authors: Ronald Edward Parr
Categories:
Type: BOOK - Published: 1998 - Publisher:

GET EBOOK

Reinforcement Learning
Language: en
Pages: 653
Authors: Marco Wiering
Categories: Technology & Engineering
Type: BOOK - Published: 2012-03-05 - Publisher: Springer Science & Business Media

GET EBOOK

Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding
Constrained Markov Decision Processes
Language: en
Pages: 256
Authors: Eitan Altman
Categories: Mathematics
Type: BOOK - Published: 2021-12-17 - Publisher: Routledge

GET EBOOK

This book provides a unified approach for the study of constrained Markov decision processes with a finite state space and unbounded costs. Unlike the single co