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

Year: 2024

Type: conference-paper

Abstract: International audience

Source: HAL (Le Centre pour la Communication Scientifique Directe)

Authors: Mastropietro, Daniel, Ayesta, Urtzi, Jonckheere, Matthieu

Institution: Laboratoire d'Analyse et d'Architecture des Systèmes

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4406960602

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