Hierarchical clustering optimizes the tradeoff between compositionality and expressivity of task structures for flexible reinforcement learning

DOI10.1016/j.artint.2022.103770OpenAlexW4292342011MaRDI QIDQ2093367

Michael J. Frank, Rex G. Liu

Publication date: 8 November 2022

Published in: Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.artint.2022.103770

zbMATH Keywords

reinforcement learningplanningmodel-based reinforcement learninghierarchical reinforcement learningcognitive sciencescompositional generalization

Mathematics Subject Classification ID

Artificial intelligence (68Txx)

Cites Work

• Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
• A Bayesian analysis of some nonparametric problems
• Human-level concept learning through probabilistic program induction
• Hierarchical Dirichlet Processes
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