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.
Bibliographical noteGenerated from Scopus record by KAUST IRTS on 2022-09-14
ASJC Scopus subject areas
- Behavioral Neuroscience
- Experimental and Cognitive Psychology