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Coordinating transportation services in a hospital environment using Deep Reinforcement Learning
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Computing Science.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Computing Science.
2018 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Artificial Intelligence has in the recent years become a popular subject, many thanks to the recent progress in the area of Machine Learning and particularly to the achievements made using Deep Learning. When combining Reinforcement Learning and Deep Learning, an agent can learn a successful behavior for a given environment. This has opened the possibility for a new domain of optimization. This thesis evaluates if a Deep Reinforcement Learning agent can learn to aid transportation services in a hospital environment. A Deep Q-learning Networkalgorithm (DQN) is implemented, and the performance is evaluated compared to a Linear Regression-, a random-, and a smart agent. The result indicates that it is possible for an agent to learn to aid transportation services in a hospital environment, although it does not outperform linear regression on the most difficult task. For the more complex tasks, the learning process of the agent is unstable, and implementation of a Double Deep Q-learning Network may stabilize the process. An overall conclusion is that Deep Reinforcement Learning can perform well on these types of problems and more applied research may result in greater innovations.

Place, publisher, year, edition, pages
2018. , p. 76
Series
UPTEC STS, ISSN 1650-8319 ; 18013
Keywords [en]
Artificial Intelligence, Machine Learning, Deep Learning, Reinforcement Learning, Transportation, Q-learning
Keywords [sv]
artificiell intelligens, maskininlärning, djupinlärning, transport
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-355737OAI: oai:DiVA.org:uu-355737DiVA, id: diva2:1230827
External cooperation
B&M Systemutveckling
Educational program
Systems in Technology and Society Programme
Presentation
2018-06-14, Å64119, Lägerhyddsvägen 1, Uppsala, 17:15 (English)
Supervisors
Examiners
Available from: 2018-07-04 Created: 2018-07-04 Last updated: 2018-07-04Bibliographically approved

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CiteExportLink to record
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Output format
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