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Deep Reinforcement Learning for Intelligent Road Maintenance in Small Island Developing States Vulnerable to Climate Change: Using Artificial Intelligence to Adapt Communities to Climate Change
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
2018 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The consequences of climate change are already noticeable in small island developing states. Road networks are crucial for a functioning society, and are particularly vulnerable to extreme weather, floods, landslides and other effects of climate change. Road systems in small island developing states are therefore in special need of climate adaptation efforts. Climate adaptation of road systems also has to be cost-efficient since these small island states have limited economical resources. Recent advances in deep reinforcement learning, a subfield of artificial intelligence, has proven that intelligent agents can achieve superhuman level at a number of tasks, setting hopes high for possible future applications of the algorithms. To investigate wether deep reinforcement learning is suitable for climate adaptation of road maintenance systems a simulator has been set up, together with three deep reinforcement learning agents, and two non-intelligent agents for performance comparisons. The results of the project indicate that deep reinforcement learning is suitable for use in intelligent road maintenance systems for climate adaptation in small island developing states.

Place, publisher, year, edition, pages
2018. , p. 36
Series
UPTEC F, ISSN 1401-5757 ; 18066
Keywords [en]
Deep reinforcement learning, Climate change adaptation, Machine learning
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:uu:diva-373502OAI: oai:DiVA.org:uu-373502DiVA, id: diva2:1278708
Educational program
Master Programme in Engineering Physics
Supervisors
Examiners
Available from: 2019-01-23 Created: 2019-01-14 Last updated: 2019-01-23Bibliographically approved

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CiteExportLink to record
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