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Spodbujevano učenje v vodenju in optimizaciji procesov
ID KOVAČ, IVAN (Author), ID Mušič, Gašper (Mentor) More about this mentor... This link opens in a new window

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PID: 20.500.12556/rul/0af90bfd-ca48-4e09-b412-d0ce4872205c

Abstract
Podatki postajajo glavni vir 21 stoletja. Učenje in obdelava vseh teh podatkov presega sposobnosti in zmogljivosti človeka zato je uporaba strojev neizbežna. Med naborom paradigem strojnega učenja je posebej zanimivo spodbujevano učenje vendar kako se to umešča v vodenje procesov, kakšne so posebnosti, delovni okvirji, teh informacij ni na voljo. V okviru naloge smo raziskali in preučili teoretično osnovo paradigme, različne scenarije in problematike ter preizkusili in medsebojno primerjali nekatera delovna okolja. Rezultat je umestitev paradigme v področje vodenja in optimizacije ter pregled strojnega učenja na splošno. Glavni del predstavlja ključne gradnike in teoretično osnovo paradigme s pregledom glavnih algoritmov in njihovih lastnosti in tipičnih scenarijev uporabe in problematik znotraj same paradigme. Vsebinsko so predstavljane tri javno dostopne odprtokodne knjižnice in ena spletna storitev, ki kot take predstavljajo delovna in razvoja okolja. Nakazane so smernice in izhodišča za nadaljevanje študija in raziskovanja. Čeprav so algoritmi spodbujevanega učenja počasnejši v primerjavi z algoritmi v drugih paradigmah učenja, imajo širše področje uporabe in potencial za izgradnjo boljših samo učečih se strojev.

Language:Slovenian
Keywords:strojno učenje, spodbujevano učenje, Markovski proces odločanja, funkcija vrednosti, optimalna politika
Work type:Undergraduate thesis
Organization:FE - Faculty of Electrical Engineering
Year:2016
PID:20.500.12556/RUL-83472 This link opens in a new window
Publication date in RUL:16.06.2016
Views:1386
Downloads:441
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Secondary language

Language:English
Title:Reinforcement learning in process control and optimization
Abstract:
Data is becoming the prime 21st century resource. Learning and processing all of this data surpasses human capability and capacity, meaning machines are unavoidable. Amongst the many machine learning paradigms, Reinforcement Learning is of especial interest; however, there is no information as to how the latter be included in process management, specifics and frameworks. Within the framework of this thesis, we researched and examined the theoretical basis for this paradigm, the various scenarios and problems, and tested and compared some of work environments, resulting in the paradigm’s inclusion in the area of processes control and optimisation, as well as providing an overview of machine learning in general. The bulk of this work presents the key building blocks and basis for the paradigm, focusing on its main algorithms and their characteristics. It also presents typical use scenarios and inherent problems within the paradigm itself. We present three public open-source libraries and one web-based service as examples of work and development environments. This thesis also presents guidelines and starting points for further study and research. Even though reinforced learning algorithms are slower when compared to other learning paradigms, they have a much wider scope of use and the potential to produce better autonomous learning machines.

Keywords:Machine Learning, Reinforcement Learning, Markov Decision Proces, Value Function, Optimal Policy

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