HQ-learning

Marco Wiering, Jürgen Schmidhuber

Research output: Contribution to journalArticlepeer-review

121 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 languageEnglish (US)
Pages (from-to)219-246
Number of pages28
JournalAdaptive Behavior
Volume6
Issue number2
DOIs
StatePublished - Jan 1 1997
Externally publishedYes

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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