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Adaptive Behavior
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HQ-Learning

Marco Wiering

Istituto Dalle Molle di Studi sull'Intelligenza Artificiale

Jürgen Schmidhuber

Istituto Dalle Molle di Studi sull'Intelligenza Artificiale

HQ-learning is a hierarchical extension of Q({lambda})-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.

Key Words: reinforcement learning • hierarchical Q-learning • POMDPs ; non-Markov • subgoal learning

Adaptive Behavior, Vol. 6, No. 2, 219-246 (1997)
DOI: 10.1177/105971239700600202
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