The SoS conductor: Orchestrating resources with iterative agent‐based reinforcement learning
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Wiley Open Access
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| Format: | Artículo Open Access |
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Wiley
2024
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| _version_ | 1867003252799700992 |
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| author | Qiliang Chen Babak Heydari |
| author_facet | Qiliang Chen Babak Heydari Qiliang Chen Babak Heydari |
| collection | Wiley Open Access |
| contents | The SoS conductor: Orchestrating resources with iterative agent‐based reinforcement learning Qiliang Chen Babak Heydari Systems Engineering Abstract We introduce a novel resource management approach for Systems of Systems (SoS), utilizing hierarchical deep reinforcement learning, iterating with agent‐based simulation. A key innovation of this method is its ability to balance top‐down SoS management with the autonomy of individual systems. This is achieved by dynamically allocating resources to each system, thereby modifying the range of options they can autonomously choose from. This dynamic option adjustment is a powerful approach to managing the trade‐off between centralized efficiency and decentralized autonomous actions of the systems, enabling the SoS to maintain the systems' autonomy while ensuring efficient SoS governance. The method, validated through a case study, not only demonstrates the potential and efficacy of the learning framework but also reveals how, using this method, minor performance sacrifices can lead to substantial improvements in resource efficiency. 10.1002/sys.21747 http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| doi_str_mv | 10.1002/sys.21747 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_sys_21747 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| publishDate | 2024 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | The SoS conductor: Orchestrating resources with iterative agent‐based reinforcement learning Qiliang Chen Babak Heydari Systems Engineering The SoS conductor: Orchestrating resources with iterative agent‐based reinforcement learning Qiliang Chen Babak Heydari Systems Engineering Abstract We introduce a novel resource management approach for Systems of Systems (SoS), utilizing hierarchical deep reinforcement learning, iterating with agent‐based simulation. A key innovation of this method is its ability to balance top‐down SoS management with the autonomy of individual systems. This is achieved by dynamically allocating resources to each system, thereby modifying the range of options they can autonomously choose from. This dynamic option adjustment is a powerful approach to managing the trade‐off between centralized efficiency and decentralized autonomous actions of the systems, enabling the SoS to maintain the systems' autonomy while ensuring efficient SoS governance. The method, validated through a case study, not only demonstrates the potential and efficacy of the learning framework but also reveals how, using this method, minor performance sacrifices can lead to substantial improvements in resource efficiency. 10.1002/sys.21747 http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| title | The SoS conductor: Orchestrating resources with iterative agent‐based reinforcement learning |
| topic | Systems Engineering |
| url | https://incose.onlinelibrary.wiley.com/doi/10.1002/sys.21747 |