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of 3 298
pro vyhledávání: '"Bottero P"'
Autor:
Gemma Errico
Publikováno v:
Encyclopaideia, Vol 28, Iss 68, Pp 85-87 (2024)
Externí odkaz:
https://doaj.org/article/f843bc1d09364b2eb1c1ee115cafb75d
Autor:
Alessandra Augelli
Publikováno v:
Encyclopaideia, Vol 27, Iss 65, Pp 115-116 (2023)
Externí odkaz:
https://doaj.org/article/d3eaf87098a545c98d09d585a25b09ca
Optimal decision-making under partial observability requires reasoning about the uncertainty of the environment's hidden state. However, most reinforcement learning architectures handle partial observability with sequence models that have no internal
Externí odkaz:
http://arxiv.org/abs/2409.16824
We consider a sequential decision making task, where the goal is to optimize an unknown function without evaluating parameters that violate an a~priori unknown (safety) constraint. A common approach is to place a Gaussian process prior on the unknown
Externí odkaz:
http://arxiv.org/abs/2402.15347
We consider the problem of quantifying uncertainty over expected cumulative rewards in model-based reinforcement learning. In particular, we focus on characterizing the variance over values induced by a distribution over Markov decision processes (MD
Externí odkaz:
http://arxiv.org/abs/2312.04386
Autor:
Caterina Froiio, Laura Torselli, Luca Bottero, Nirvana Maroni, Dario Palmisano, Pasquale Chiacchio, Cristian Giuseppe Monaco, Laura Palvarini, Giovanni Pompili, Andrea Pisani Ceretti
Publikováno v:
Laparoscopic, Endoscopic and Robotic Surgery, Vol 7, Iss 4, Pp 186-189 (2024)
Externí odkaz:
https://doaj.org/article/bbe14021edeb44989d879e8e4402ee9b
Quantifying uncertainty about a policy's long-term performance is important to solve sequential decision-making tasks. We study the problem from a model-based Bayesian reinforcement learning perspective, where the goal is to learn the posterior distr
Externí odkaz:
http://arxiv.org/abs/2308.06590
Akademický článek
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We consider the problem of quantifying uncertainty over expected cumulative rewards in model-based reinforcement learning. In particular, we focus on characterizing the variance over values induced by a distribution over MDPs. Previous work upper bou
Externí odkaz:
http://arxiv.org/abs/2302.12526
Publikováno v:
Frontiers in Immunology, Vol 15 (2024)
IntroductionWe previously identified Bordetella pertussis-derived outer membrane vesicles (OMVs) as a promising immunogen for improving pertussis vaccines. In this study, we evaluated the efficacy of our vaccine prototype in immunization strategies a
Externí odkaz:
https://doaj.org/article/ccb7c437030f4035af1a6a9fbf64e1c0