Variational Information Bottleneck Regularized Deep Reinforcement Learning for Efficient Robotic Skill Adaptation

Deep Reinforcement Learning (DRL) algorithms have been widely studied for sequential decision-making problems, and substantial progress has been achieved, especially in autonomous robotic skill learning. However, it is always difficult to deploy DRL methods in practical safety-critical robot systems...

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Detalles Bibliográficos
Autores principales: Xiang, Guofei, Dian, Songyi, Du, Shaofeng, Lv, Zhonghui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9864208/
https://www.ncbi.nlm.nih.gov/pubmed/36679561
http://dx.doi.org/10.3390/s23020762

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9864208/
https://www.ncbi.nlm.nih.gov/pubmed/36679561
http://dx.doi.org/10.3390/s23020762

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