Learning State-Variable Relationships in POMCP: A Framework for Mobile Robots

We address the problem of learning relationships on state variables in Partially Observable Markov Decision Processes (POMDPs) to improve planning performance. Specifically, we focus on Partially Observable Monte Carlo Planning (POMCP) and represent the acquired knowledge with a Markov Random Field...

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Detalles Bibliográficos
Autores principales: Zuccotto, Maddalena, Piccinelli, Marco, Castellini, Alberto, Marchesini, Enrico, Farinelli, Alessandro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Robotics and AI
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9343685/
https://www.ncbi.nlm.nih.gov/pubmed/35928541
http://dx.doi.org/10.3389/frobt.2022.819107

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9343685/
https://www.ncbi.nlm.nih.gov/pubmed/35928541
http://dx.doi.org/10.3389/frobt.2022.819107

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