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Titel:

Data-Informed Residual Reinforcement Learning for High-Dimensional Robotic Tracking Control

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Li, Cong; Liu, Fangzhou; Wang, Yongchao; Buss, Martin
Abstract:
The learning inefficiency of reinforcement learning (RL) from scratch hinders its practical application toward continuous robotic tracking control, especially for high-dimensional robots. This article proposes a data-informed residual reinforcement learning (DR-RL)-based robotic tracking control scheme applicable to robots with high dimensionality. The proposed DR-RL methodology outperforms common RL methods regarding sample efficiency and scalability. Specifically, we first decouple the origina...     »
Zeitschriftentitel:
IEEE/ASME Transactions on Mechatronics
Jahr:
2024
Jahr / Monat:
2024-09
Monat:
Sep
Reviewed:
ja
Sprache:
en
Verlag / Institution:
IEEE
Publikationsdatum:
23.09.2024
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