Int-HRL: towards intention-based hierarchical reinforcement learning.

Anna Penzkofer, Simon Schaefer, Florian Strohm, Mihai Bâce, Stefan Leutenegger, Andreas Bulling

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In one paragraph

Article in Neural computing & applications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Anna PenzkoferInstitute for Visualisation and Interactive Systems, University of Stuttgart, Pfaffenwaldring 5A, 70569 Stuttgart, Germany.ORCID 0009-0006-8570-0007

Simon SchaeferMachine Learning for Robotics, Technical University of Munich, Boltzmannstrasse 3, 85748 Munich, Germany.

Florian StrohmInstitute for Visualisation and Interactive Systems, University of Stuttgart, Pfaffenwaldring 5A, 70569 Stuttgart, Germany.

Mihai BâceDepartment of Computer Science, KU Leuven, Andreas Vesaliusstraat 13, box 2600, 3000 Leuven, Belgium.

Stefan LeuteneggerMachine Learning for Robotics, Technical University of Munich, Boltzmannstrasse 3, 85748 Munich, Germany.

Andreas BullingInstitute for Visualisation and Interactive Systems, University of Stuttgart, Pfaffenwaldring 5A, 70569 Stuttgart, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

While deep reinforcement learning (RL) agents outperform humans on an increasing number of tasks, training them requires data equivalent to decades of human gameplay. Recent hierarchical RL methods have increased sample efficiency by incorporating information inherent to the structure of the decision problem but at the cost of having to discover or use human-annotated sub-goals that guide the learning process. We show that intentions of human players, i.e. the precursor of goal-oriented decisions, can be robustly predicted from eye gaze even for the long-horizon sparse rewards task of Montezuma's Revenge-one of the most challenging RL tasks in the Atari2600 game suite. We propose

Indexed as

Eye gazeHierarchical reinforcement learningIntention predictionMontezuma’s revengeSub-goal extraction

Identifiers

PMID40756565

DOI10.1007/s00521-024-10596-2

PMCPMC12313806

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.