Bottom-up learning of hierarchical models in a class of deterministic POMDP environments

Bottom-up learning of hierarchical models in a class of deterministic POMDP environments

The theory of partially observable Markov decision processes (POMDPs) is a useful tool for developing various intelligent agents, and learning hierarchical POMDP models is one of the key approaches for building such agents when the environments of the agents are unknown and large. To learn hierarchi...

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Bibliographic Details
Main Authors: Itoh Hideaki, Fukumoto Hisao, Wakuya Hiroshi, Furukawa Tatsuya
Format: Article
Language:English
Published: Sciendo 2015-09-01
Series:International Journal of Applied Mathematics and Computer Science
Subjects:
partially observable markov decision processes
hierarchical models
bottom-up learning
Online Access:https://doi.org/10.1515/amcs-2015-0044

Internet

https://doi.org/10.1515/amcs-2015-0044

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