HQ-learning

Marco Wiering; Jürgen Schmidhuber

doi:10.1177/105971239700600202

HQ-learning

Marco Wiering, Jürgen Schmidhuber

Research output: Contribution to journal › Article › peer-review

133 Scopus citations

Abstract

HQ-learning is a hierarchical extension of Q(λ)-learning designed to solve certain types of partially observable Markov decision problems (POMDPs). HQ automatically decomposes POMDPs into sequences of simpler subtasks that can be solved by memoryless policies learnable by reactive subagents. HQ can solve partially observable mazes with more states than those used in most previous POMDP work.

Original language	English (US)
Pages (from-to)	219-246
Number of pages	28
Journal	Adaptive Behavior
Volume	6
Issue number	2
DOIs	https://doi.org/10.1177/105971239700600202
State	Published - Jan 1 1997
Externally published	Yes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14

ASJC Scopus subject areas

Behavioral Neuroscience
Experimental and Cognitive Psychology

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10.1177/105971239700600202

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HQ-learning

Abstract

Bibliographical note

ASJC Scopus subject areas

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