Comparing Deep Reinforcement Learning Algorithms’ Ability to Safely Navigate Challenging Waters

Reinforcement Learning (RL) controllers have proved to effectively tackle the dual objectives of path following and collision avoidance. However, finding which RL algorithm setup optimally trades off these two tasks is not necessarily easy. This work proposes a methodology to explore this that lever...

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Detalles Bibliográficos
Autores principales: Larsen, Thomas Nakken, Teigen, Halvor Ødegård, Laache, Torkel, Varagnolo, Damiano, Rasheed, Adil
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Robotics and AI
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8473616/
https://www.ncbi.nlm.nih.gov/pubmed/34589522
http://dx.doi.org/10.3389/frobt.2021.738113

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8473616/
https://www.ncbi.nlm.nih.gov/pubmed/34589522
http://dx.doi.org/10.3389/frobt.2021.738113

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