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Title:
SAMOOJAČITVENO UČENJE
Authors:
ID
Mlakar, Matej
(Author)
ID
Strnad, Damjan
(Mentor)
More about this mentor...
Files:
UNI_Mlakar_Matej_2012.pdf
(1,71 MB)
MD5: 1B2A4260CAD8EE7A2072F6E21619AE90
PID:
20.500.12556/dkum/e00e27d0-f18d-4ddb-b3a6-34e2e97fa37c
Language:
Slovenian
Work type:
Bachelor thesis/paper
Typology:
2.11 - Undergraduate Thesis
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
V diplomskem delu predstavljamo samoojačitveno učenje, ki je področje strojnega učenja in se ukvarja z vprašanjem, kako naj agent deluje v okolju, da doseže čim večjo nagrado. V nalogi opravimo splošen pregled te teme, nato podrobneje opišemo nekaj pomembnejših metod, eno izmed njih pa implementiramo v mrežnem okolju lovec-plen. Na koncu predstavimo še naš program ter analiziramo dobljene rezultate.
Keywords:
strojno učenje
,
nenadzorovano učenje
,
mrežno okolje lovec-plen
Place of publishing:
Maribor
Publisher:
[M. Mlakar]
Year of publishing:
2012
PID:
20.500.12556/DKUM-36637
UDC:
004.89:004.4(043.2)
COBISS.SI-ID:
16232214
NUK URN:
URN:SI:UM:DK:KU334R2T
Publication date in DKUM:
11.07.2012
Views:
2064
Downloads:
170
Metadata:
Categories:
KTFMB - FERI
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Vancouver
:
MLAKAR, Matej, 2012,
SAMOOJAČITVENO UČENJE
[online]. Bachelor’s thesis. Maribor : M. Mlakar. [Accessed 5 April 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=36637
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Hover the mouse pointer over a document title to show the abstract or click on the title to get all document metadata.
Secondary language
Language:
English
Title:
REINFORCEMENT LEARNING
Abstract:
In this diploma work we present reinforcement learning, which is an area of machine learning that studies the question of how an agent ought to act in an environment to achieve maximum reward. In this work we take a general look at the topic, then describe a few of the more important methods in detail and implement one of them in the predator-prey grid world domain. In the end, we present our program and analyze its results.
Keywords:
machine learning
,
unsupervised learning
,
predator-prey grid world
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