From Semantics to Execution: Integrating Action Planning With Reinforcement Learning for Robotic Causal Problem-Solving

Reinforcement learning is generally accepted to be an appropriate and successful method to learn robot control. Symbolic action planning is useful to resolve causal dependencies and to break a causally complex problem down into a sequence of simpler high-level actions. A problem with the integration...

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Detalles Bibliográficos
Autores principales: Eppe, Manfred, Nguyen, Phuong D. H., Wermter, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Robotics and AI
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805615/
https://www.ncbi.nlm.nih.gov/pubmed/33501138
http://dx.doi.org/10.3389/frobt.2019.00123

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805615/
https://www.ncbi.nlm.nih.gov/pubmed/33501138
http://dx.doi.org/10.3389/frobt.2019.00123

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