Moment-Based Reinforcement Learning for Ensemble Control Article Swipe
Yao‐Chi Yu
,
Vignesh Narayanan
,
Jr-Shin Li
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/tnnls.2023.3264151
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/tnnls.2023.3264151
Problems involving controlling the collective behavior of a population of structurally similar dynamical systems, the so-called ensemble control, arise in diverse emerging applications and pose a grand challenge in systems science and control engineering. Owing to the severely under-actuated nature and the difficulty of placing large-scale sensor networks, ensemble systems are limited to being actuated and monitored at the population level. Moreover, mathematical models describing the dynamics of ensemble systems are often elusive. Therefore, it is essential to design broadcast controls that excite the entire population in such a way that the heterogeneity in system dynamics is robustly compensated. In this article, we propose a reinforcement learning (RL)-based data-driven control framework incorporating population-level aggregated measurement data to learn a global control signal for steering a dynamic population in the desired manner. In particular, we introduce the notion of ensemble moments induced by aggregated measurements and derive the associated moment system to the original ensemble system. Then, using the moment system, we learn an approximation of optimal value functions and the associated policies in terms of ensemble moments through RL. We illustrate the feasibility and scalability of the proposed moment-based approach via numerical experiments using a population of linear, bilinear, and nonlinear dynamic ensemble systems. We report that the proposed method achieves the desired control objectives of various ensemble control tasks and obtains significantly better averaged-reward when compared with three existing methods.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tnnls.2023.3264151
- OA Status
- green
- Cited By
- 6
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4365135903
All OpenAlex metadata
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https://openalex.org/W4365135903Canonical identifier for this work in OpenAlex
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https://doi.org/10.1109/tnnls.2023.3264151Digital Object Identifier
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Moment-Based Reinforcement Learning for Ensemble ControlWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-04-12Full publication date if available
- Authors
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Yao‐Chi Yu, Vignesh Narayanan, Jr-Shin LiList of authors in order
- Landing page
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https://doi.org/10.1109/tnnls.2023.3264151Publisher landing page
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://pmc.ncbi.nlm.nih.gov/articles/PMC10676148/Direct OA link when available
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Population, Reinforcement learning, Moment (physics), Computer science, Scalability, Nonlinear system, Artificial intelligence, Ensemble learning, Control theory (sociology), Control (management), Physics, Classical mechanics, Quantum mechanics, Demography, Sociology, DatabaseTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2024: 5, 2023: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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