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008 210111s2021 sz | o |||| 0|eng d
020 _a9783030618674
024 7 _a10.1007/978-3-030-61867-4
_2doi
040 _aES-VaU
_bspa
_cES-VaU
_dES-VaU
050 4 _aT57.6-.97
_b2021 EB
100 1 _aBrandimarte, Paolo.
_eautor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 0 0 _aFrom Shortest Paths to Reinforcement Learning :
_bA MATLAB-Based Tutorial on Dynamic Programming
_cby Paolo Brandimarte
250 _a1st ed. 2021.
264 1 _aCham
_c2021
_bSpringer International Publishing
300 _a1 recurso en línea
336 _atexto
_btxt
_2rdacontent
337 _aelectrónico
_bc
_2rdamedia
338 _arecurso electrónico
_bcr
_2rdacarrier
490 0 _aEURO Advanced Tutorials on Operational Research
_x2364-6888
505 0 _aThe dynamic programming principle -- Implementing dynamic programming -- Modeling for dynamic programming -- Numerical dynamic programming for discrete states -- Approximate dynamic programming and reinforcement learning for discrete states -- Numerical dynamic programming for continuous states -- Approximate dynamic programming and reinforcement learning for continuous states.
520 _aDynamic programming (DP) has a relevant history as a powerful and flexible optimization principle, but has a bad reputation as a computationally impractical tool. This book fills a gap between the statement of DP principles and their actual software implementation. Using MATLAB throughout, this tutorial gently gets the reader acquainted with DP and its potential applications, offering the possibility of actual experimentation and hands-on experience. The book assumes basic familiarity with probability and optimization, and is suitable to both practitioners and graduate students in engineering, applied mathematics, management, finance and economics.
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-61867-4
_zAcceso a este recurso digital (usuarios Universidad Europea de Valencia)
942 _2lcc
_cLE
988 _aSpringer_Business_2021
999 _c239093
_d239093