Sample-efficient reinforcement learning for environments with rare high-reward states

Authors

Daniel Mastropietro (ORCID: https://orcid.org/0000-0002-9456-7475)
Matthieu Jonckheere (ORCID: https://orcid.org/0000-0003-3576-5866)
Urtzi Ayesta (ORCID: https://orcid.org/0000-0003-1761-2313)

Institutions

Laboratoire d'Analyse et d'Architecture des Systèmes (FR)

Publication Details

Journal: SPIRE - Sciences Po Institutional REpository
Published: 2024-10-28
Primary Topic: Reinforcement Learning in Robotics
Type: article
Field-Weighted Citation Impact: 0.00

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article

Sample-efficient reinforcement learning for environments with rare high-reward states

Daniel Mastropietro, Matthieu Jonckheere, Urtzi Ayesta

Oct 28, 2024

SPIRE - Sciences Po Institutional REpository

Reinforcement Learning in Robotics

article

Sample-efficient reinforcement learning for environments with rare high-reward states

Daniel Mastropietro, Matthieu Jonckheere, Urtzi Ayesta

October 28, 2024article en

Abstract

No abstract available for this paper.

SPIRE - Sciences Po Institutional REpository

Laboratoire d'Analyse et d'Architecture des Systèmes (FR)

Openalex Percentile: Top 76%

Reinforcement Learning in Robotics

Open Access PDF OpenAlex

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Sample-efficient reinforcement learning for environments with rare high-reward states — Daniel Mastropietro, Matthieu Jonckheere, et al. · SPIRE - Sciences Po Institutional REpository (2024) | TGRS Research Map | TGRS