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Deep Reinforcement ...
Deep Reinforcement Learning in a Dynamic Environment : A Case Study in the Telecommunication Industry
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- Zhang, Hongyi (författare)
- Chalmers University of Technology,Gothenburg,Sweden
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- Li, Jingya (författare)
- Ericsson Research,Ericsson
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- Qi, Zhiqiang (författare)
- Ericsson Research,Ericsson
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- Lin, Xingqin (författare)
- Ericsson Research,Ericsson
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- Aronsson, Anders (författare)
- Ericsson Research,Ericsson
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- Bosch, Jan (författare)
- Chalmers University of Technology,Gothenburg,Sweden
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- Olsson, Helena Holmström (författare)
- Malmö universitet,Institutionen för datavetenskap och medieteknik (DVMT)
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2022
- 2022
- Engelska.
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Ingår i: 2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665461528 - 9781665461535
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
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- Reinforcement learning, particularly deep reinforcement learning, has made remarkable progress in recent years and is now used not only in simulators and games but is also making its way into embedded systems as another software-intensive domain. However, when implemented in a real-world context, reinforcement learning is typically shown to be fragile and incapable of adapting to dynamic environments. In this paper, we provide a novel dynamic reinforcement learning algorithm for adapting to complex industrial situations. We apply and validate our approach using a telecommunications use case. The proposed algorithm can dynamically adjust the position and antenna tilt of a drone-based base station to maintain reliable wireless connectivity for mission-critical users. When compared to traditional reinforcement learning approaches, the dynamic reinforcement learning algorithm improves the overall service performance of a drone-based base station by roughly 20%. Our results demonstrate that the algorithm can quickly evolve and continuously adapt to the complex dynamic industrial environment.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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