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Distributed Optimisation in Multi-Agent Systems Through Deep Reinforcement Learning
KTH, School of Electrical Engineering and Computer Science (EECS).
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The increased availability of computing power have made reinforcement learning a popular field of science in the most recent years. Recently, reinforcement learning has been used in applications like decreasing energy consumption in data centers, diagnosing patients in medical care and in text-tospeech software. This project investigates how well two different reinforcement learning algorithms, Q-learning and deep Qlearning, can be used as a high-level planner for controlling robots inside a warehouse. A virtual warehouse was created, and the two different algorithms were tested. The reliability of both algorithms where found to be insufficient for real world applications but the deep Q-learning algorithm showed great potential and further research is encouraged.

Place, publisher, year, edition, pages
2019. , p. 9
Series
TRITA-EECS-EX ; 2019:127
Keywords [en]
Reinforcement learning, distributed, optimisation, Q-learning, deep Q-learning, warehouse robots, artificial neural networks.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-254227OAI: oai:DiVA.org:kth-254227DiVA, id: diva2:1329255
Subject / course
Electrical Engineering
Educational program
Master of Science in Engineering - Electrical Engineering
Supervisors
Examiners
Available from: 2019-06-24 Created: 2019-06-24 Last updated: 2019-06-24Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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  • ieee
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  • vancouver
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Language
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  • fi-FI
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  • nn-NB
  • sv-SE
  • Other locale
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Output format
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  • asciidoc
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