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A comparison of genetic algorithm and reinforcement learning for autonomous driving
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
En jämförelse mellan genetisk algoritm och förstärkningslärande för självkörande bilar (Swedish)
Abstract [en]

This paper compares two different methods, reinforcement learning and genetic algorithm for designing autonomous cars’ control system in a dynamic environment.

The research problem could be formulated as such: How is the learning efficiency compared between reinforcement learning and genetic algorithm on autonomous navigation through a dynamic environment?

In conclusion, the genetic algorithm outperforms the reinforcement learning on mean learning time, despite the fact that the prior shows a large variance, i.e. genetic algorithm provide a better learning efficiency.

Abstract [sv]

I det här papperet jämförs två olika metoder, förstärkningsinlärning och genetisk algoritm för att designa autonoma bilar styrsystem i en dynamisk miljö. Forskningsproblemet kan formuleras som: Hur är inlärningseffektiviteten jämför mellan förstärkningsinlärning och genetisk algoritm på autonom navigering i en dynamisk miljö? Sammanfattningsvis, den genetisk algoritm överträffar förstärkningsinlärning på genomsnittlig inlärningstid, trots att den tidigare visar en stor varians, dvs genetisk algoritm, ger en bättre inlärningseffektivitet.

Place, publisher, year, edition, pages
2019. , p. 32
Series
TRITA-EECS-EX ; 2019:505
Keywords [en]
Thesis, Machine learning, Genetic algorithm, Deep reinforcement learning, Autonomous driving
Keywords [sv]
Thesis, Maskininlärning, Genetisk algoritm, Djup förstärkning lärande, självkörandebilar
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-261595OAI: oai:DiVA.org:kth-261595DiVA, id: diva2:1358693
Supervisors
Examiners
Available from: 2019-10-08 Created: 2019-10-08 Last updated: 2019-10-08Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
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  • vancouver
  • Other style
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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