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Multi-Agent Reinforcement Learning in Dynamic Industrial Context
Chalmers University of Technology,Gothenburg,Sweden.
Ericsson,Ericsson Research.
Ericsson,Ericsson Research.
Ericsson,Ericsson Research.
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2023 (English)In: 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC), Institute of Electrical and Electronics Engineers (IEEE), 2023Conference paper, Published paper (Refereed)
Abstract [en]

Deep reinforcement learning has advanced signifi-cantly in recent years, and it is now used in embedded systems in addition to simulators and games. Reinforcement Learning (RL) algorithms are currently being used to enhance device operation so that they can learn on their own and offer clients better services. It has recently been studied in a variety of industrial applications. However, reinforcement learning, especially when controlling a large number of agents in an industrial environment, has been demonstrated to be unstable and unable to adapt to realistic situations when used in a real-world setting. To address this problem, the goal of this study is to enable multiple reinforcement learning agents to independently learn control policies on their own in dynamic industrial contexts. In order to solve the problem, we propose a dynamic multi-agent reinforcement learning (dynamic multi-RL) method along with adaptive exploration (AE) and vector-based action selection (VAS) techniques for accelerating model convergence and adapting to a complex industrial environment. The proposed algorithm is tested for validation in emergency situations within the telecommunications industry. In such circumstances, three unmanned aerial vehicles (UAV-BSs) are used to provide temporary coverage to mission-critical (MC) customers in disaster zones when the original serving base station (BS) is destroyed by natural disasters. The algorithm directs the participating agents automatically to enhance service quality. Our findings demonstrate that the proposed dynamic multi-RL algorithm can proficiently manage the learning of multiple agents and adjust to dynamic industrial environments. Additionally, it enhances learning speed and improves the quality of service.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023.
Series
Proceedings - International Computer Software & Applications Conference, ISSN 0730-3157
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mau:diva-63756DOI: 10.1109/compsac57700.2023.00066ISI: 001046484100056Scopus ID: 2-s2.0-85168859799ISBN: 979-8-3503-2697-0 (electronic)ISBN: 979-8-3503-2698-7 (print)OAI: oai:DiVA.org:mau-63756DiVA, id: diva2:1813246
Conference
47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023, Torino, Italy, June 26-30, 2023
Available from: 2023-11-20 Created: 2023-11-20 Last updated: 2023-11-20Bibliographically approved

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Olsson, Helena Holmström

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