Hierarchical Learning and Planning in Partially Observable Markov Decision Processes

Hierarchical Learning and Planning in Partially Observable Markov Decision Processes
Author: Georgios Theocharous
Publisher:
Total Pages: 438
Release: 2002
Genre: Dynamic programming
ISBN:


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Hierarchical Learning and Planning in Partially Observable Markov Decision Processes
Language: en
Pages: 438
Authors: Georgios Theocharous
Categories: Dynamic programming
Type: BOOK - Published: 2002 - Publisher:

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Planning with Markov Decision Processes
Language: en
Pages: 204
Authors: Mausam Natarajan
Categories: Computers
Type: BOOK - Published: 2022-06-01 - Publisher: Springer Nature

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Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. Th
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

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Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under uncertainty as well as reinforcement learning prob
Reinforcement Learning
Language: en
Pages: 653
Authors: Marco Wiering
Categories: Technology & Engineering
Type: BOOK - Published: 2012-03-05 - Publisher: Springer Science & Business Media

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Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding
Handbook of Learning and Approximate Dynamic Programming
Language: en
Pages: 670
Authors: Jennie Si
Categories: Technology & Engineering
Type: BOOK - Published: 2004-08-02 - Publisher: John Wiley & Sons

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A complete resource to Approximate Dynamic Programming (ADP), including on-line simulation code Provides a tutorial that readers can use to start implementing t