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Curriculum learning for increasing the performance of a reinforcement learning agent in a static first-person shooter game
KTH, School of Electrical Engineering and Computer Science (EECS).
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Användning av läroplanering för att öka prestandan hos en agent som lärs upp med förstärkt inlärning i ett förstapersonsskjutspel med en statisk spelare (Swedish)
Abstract [en]

In this thesis, we trained a reinforcement learning agent using one of the most recent policy gradient methods, proximal policy optimization, in a first-person shooter game with a static player. We investigated how curriculum learning can be used to increase performance of a reinforcement learning agent. Two reinforcement learning agents were trained in two different environments. The first environment was constructed without curriculum learning and the second environment was with curriculum learning. After training the agents, the agents were placed in the same environment where we compared them based on their performance. The performance was measured by the achieved cumulative reward. The result showed that there is a difference in performance between the agents. It was concluded that curriculum learning can be used to increase the performance of a reinforcement learning agent in a first-person shooter game with a static player.

Abstract [sv]

I denna uppsats tränade vi en agent genom förstärkt djupinlärning med hjälp av en av de senaste gradientmetoderna, nämligen proximal policy optimization, i ett förstapersonsskjutspel med en statisk spelare. Vi undersökte hur läroplanering kan användas för att öka prestandan hos en agent som tränats med förstärkt inlärning. Två agenter tränades i två olika miljöer. Den första miljön använde inte läroplanering och den andra miljön använde läroplanering. Efter att ha tränat agenterna placerades de i samma miljö. Deras prestation mättes genom deras kumulativa belöning. Reslutatet påvisade att det finns en skillnad i prestanda mellan agenterna. Genom att använda läroplanering i ett förstapersonsskjutspel med en statisk spelare kunde prestandan hos en agent som tränats med förstärkt inlärning öka.

Place, publisher, year, edition, pages
2018. , p. 43
Series
TRITA-EECS-EX ; 2018:633
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-236462OAI: oai:DiVA.org:kth-236462DiVA, id: diva2:1256486
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Examiners
Available from: 2018-10-17 Created: 2018-10-17 Last updated: 2018-10-17Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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