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pro vyhledávání: '"Markovian decision processes"'
Distributionally robust offline reinforcement learning (RL) aims to find a policy that performs the best under the worst environment within an uncertainty set using an offline dataset collected from a nominal model. While recent advances in robust RL
Externí odkaz:
http://arxiv.org/abs/2411.07514
Akademický článek
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Publikováno v:
Applied Sciences, Vol 14, Iss 5, p 2115 (2024)
In the context of Iraq’s evolving transportation landscape and the strategic implications of the Belt and Road Initiative, this study pioneers a comprehensive framework for optimizing multimodal transportation systems. The study implemented a decis
Externí odkaz:
https://doaj.org/article/00a15c17938349e8aec7c830173488f9
Autor:
Fard, Mahdi Milani, Pineau, Joelle
Publikováno v:
Journal Of Artificial Intelligence Research, Volume 40, pages 1-24, 2011
Markovian processes have long been used to model stochastic environments. Reinforcement learning has emerged as a framework to solve sequential planning and decision-making problems in such environments. In recent years, attempts were made to apply m
Externí odkaz:
http://arxiv.org/abs/1401.3871
Publikováno v:
In IFAC PapersOnLine 2016 49(12):35-40
Autor:
Furukawa, Nagata
Publikováno v:
Mathematics of Operations Research, 1980 May 01. 5(2), 271-279.
Externí odkaz:
https://www.jstor.org/stable/3689155
Autor:
Satia, Jay K., Lave, Roy E.
Publikováno v:
Operations Research, 1973 May 01. 21(3), 728-740.
Externí odkaz:
https://www.jstor.org/stable/169381
Autor:
Furukawa, Nagata
Publikováno v:
The Annals of Mathematical Statistics, 1972 Oct 01. 43(5), 1612-1622.
Externí odkaz:
https://www.jstor.org/stable/2240083
Autor:
Köchel, Peter
Publikováno v:
Operations Research, 1985 Nov 01. 33(6), 1394-1398.
Externí odkaz:
https://www.jstor.org/stable/170646