Vahvistettu oppiminen ja sen sovellukset

Tässä kirjallisuuskatsauksessa tutustutaan vahvistettuun oppimiseen, joka on koneoppimisen menetelmä. Tavoite on käydä läpi koneoppimisen ja syväoppimisen menetelmiä ja verrata vahvistettua oppimista näihin. Vahvistetussa oppimisessa tutustutaan eri menetelmiin oppia ympäristöiltä ja lopuksi tutustu...

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Main Author: Haaralahti, Elias
Other Authors: Informaatioteknologian tiedekunta, Faculty of Information Technology, Informaatioteknologia, Information Technology, Jyväskylän yliopisto, University of Jyväskylä
Format: Bachelor's thesis
Language:fin
Published: 2019
Subjects:
Online Access: https://jyx.jyu.fi/handle/123456789/64385
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author Haaralahti, Elias
author2 Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä
author_facet Haaralahti, Elias Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä Haaralahti, Elias Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä
author_sort Haaralahti, Elias
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description Tässä kirjallisuuskatsauksessa tutustutaan vahvistettuun oppimiseen, joka on koneoppimisen menetelmä. Tavoite on käydä läpi koneoppimisen ja syväoppimisen menetelmiä ja verrata vahvistettua oppimista näihin. Vahvistetussa oppimisessa tutustutaan eri menetelmiin oppia ympäristöiltä ja lopuksi tutustutaan muutamiin vahvistetun oppimisen sovelluksiin. Lopussa todetaan vahvistetun oppimisen olevan hyödyllinen menetelmä ongelmiin, joissa agentti voi oppia ympäristön palautteen avulla. In this literature review the topic of reinforcement learning, which is a method of machine learning, will be introduced. The goal is to understand machine learning and deep learning methods and compare them to reinforcement learning methods. Reinforcement learning methods will be explored along a couple of real life applications. The conclusion is that reinforcement learning is a good method for problems, in which an agent can learn from the environment's feedback.
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spellingShingle Haaralahti, Elias Vahvistettu oppiminen ja sen sovellukset vahvistettu oppiminen Q-oppiminen Tietotekniikka Mathematical Information Technology 602 algoritmit koneoppiminen tekoäly neuroverkot
title Vahvistettu oppiminen ja sen sovellukset
title_full Vahvistettu oppiminen ja sen sovellukset
title_fullStr Vahvistettu oppiminen ja sen sovellukset Vahvistettu oppiminen ja sen sovellukset
title_full_unstemmed Vahvistettu oppiminen ja sen sovellukset Vahvistettu oppiminen ja sen sovellukset
title_short Vahvistettu oppiminen ja sen sovellukset
title_sort vahvistettu oppiminen ja sen sovellukset
title_txtP Vahvistettu oppiminen ja sen sovellukset
topic vahvistettu oppiminen Q-oppiminen Tietotekniikka Mathematical Information Technology 602 algoritmit koneoppiminen tekoäly neuroverkot
topic_facet 602 Mathematical Information Technology Q-oppiminen Tietotekniikka algoritmit koneoppiminen neuroverkot tekoäly vahvistettu oppiminen
url https://jyx.jyu.fi/handle/123456789/64385 http://www.urn.fi/URN:NBN:fi:jyu-201906053000
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