Personalized talent switch optimization in swimming coaching via multi-agent reinforcement studying pushed digital twin environments

Vilas-Boas, J. P. & Sanders, R. Swimming biomechanics: an editorial. Sports Biomech. 22 (12), 1433–1436. (2023).

Google Scholar 

De Souza Castro, F., Figueiredo, P., Toubekis, A. G., Barbosa, T. M. & McCabe, C. Editorial: physiological and Biomechanical determinants of swimming efficiency—2. Front. Sports Act. Living. 5, 1142336. (2023).

Google Scholar 

Vilas-Boas, J. P. Swimming biomechanics: from the pool to the lab … and again. Sports Biomech. 24 (10), 1197–1219. (2023).

Google Scholar 

Staunton, C. A., Romann, M., Björklund, G. & Born, D. P. Streamlining efficiency prediction: data-driven KPIs in all swimming strokes. BMC Res. Notes. 17, 52. (2024).

Google Scholar 

Yang, Z., Wang, Q. & Zhang, S. Review of computational fluid dynamics evaluation in biomimetic functions for underwater autos. Biomimetics 9 (2), 79. (2024).

Google Scholar 

Cooper, C. et al. Future analysis instructions in clever swimming coaching environments. Artif. Intell. Rev. 57 (11), 289 (2024).

Google Scholar 

Takagi, H. et al. How do swimmers management their entrance crawl swimming velocity? Current data and gaps from hydrodynamic views. Sports Biomech. 22 (12), 1552–1571. (2023).

Google Scholar 

Douglass, Ok., Stanton, J. & Stewart, M. Swimming in information. Sci. Am. 331 (2), 56–63. (2024).

Google Scholar 

Chen, L., Huang, Ok., Zhu, S. & Wang, T. A scientific assessment and meta-analysis: Biomechanical analysis of the effectiveness of energy and conditioning coaching packages on entrance crawl swimming efficiency. Front. Physiol. 12, 719103. (2024).

Google Scholar 

Meta, I. et al. The camp nou stadium as a testbed for metropolis physiology: a modular framework for city digital twins. Complexity 1–15. (2021). https://doi.org/10.1155/2021/9731180.

Tzachor, A., Sabri, S., Richards, C. E., Rajabifard, A. & Acuto, M. Potential and limitations of digital twins to attain the sustainable growth targets. Nat. Sustain. 5 (10), 822–829. (2022).

Google Scholar 

Glebova, E., Hadzic, V. & Desbordes, M. Sports venue digital twin know-how from a spectator digital visiting perspective. Front. Sports Act. Living. 5, 1289140. (2023).

Google Scholar 

Elwany, A., Mokni, C., Khriji, L. & Kanoun, O. Digital twins in sport: ideas, taxonomies, challenges and sensible potentials. Expert Syst. Appl. 252, 119717. (2024).

Google Scholar 

Hasan, H., Jaitner, T. & Steinmann, P. Digital twin teaching for bodily actions: a survey. Sensors 24 (12), 3894. (2024).

Google Scholar 

Miehe, R., Waltersmann, L., Sauer, A. & Bauernhansl, T. Sustainable manufacturing and the position of digital twins–primary reflections and views. J. Adv. Manuf. Process. 3 (2), e10078. (2021).

Google Scholar 

Albrecht, S. V., Christianos, F. & Schäfer, L. Multi-agent Reinforcement Learning: Foundations and Modern Approaches (MIT Press, 2024).

Huh, D. & Mohapatra, P. Multi-agent reinforcement studying: a complete survey. ArXiv Preprint. (2024). arXiv:2312.10256.

Google Scholar 

Li, X., Luo, F. & Li, C. Multi-agent deep reinforcement learning-based autonomous decision-making framework for neighborhood digital energy crops. Appl. Energy. 360, 122813. (2024).

Google Scholar 

Zhang, C., Abdallah, S. & Lesser, V. A survey on multi-agent reinforcement studying and its software. J. Artif. Intell. 2 (1), 42–68. (2024).

Google Scholar 

Papoudakis, G., Christianos, F., Schäfer, L. & Albrecht, S. V. A survey of multi-agent deep reinforcement studying with communication. Auton. Agent. Multi-Agent Syst. 38 (1), 1–44. (2024).

Google Scholar 

Chen, Ok. et al. Recent advances in multi-agent reinforcement studying for clever automation and management of water setting programs. Appl. Sci. 13 (6), 503. (2025).

Google Scholar 

Zheng, L. et al. Multi-agent deep reinforcement studying for coordinated multipoint in cell networks. IEEE Trans. Wireless Commun. 22 (9), 6234–6248. (2021).

Google Scholar 

Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for quick adaptation of deep networks. Proc. thirty fourth Int. Conf. Mach. Learn. 70, 1126–1135. (2017).

Google Scholar 

Sinha, S. et al. MAML-en-LLM: mannequin agnostic meta-training of LLMs for improved in-context studying. Proceedings of the thirtieth ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 3245–3256. (2024). https://doi.org/10.1145/3637528.3671905.

Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Model-agnostic meta-learning methods: a state-of-the-art brief assessment. IEEE Access. 11, 65432–65448. (2023).

Google Scholar 

Chen, Y. et al. Meta-learning for few-shot studying: a complete survey. IEEE Trans. Pattern Anal. Mach. Intell. 46 (8), 5558–5575. (2024).

Google Scholar 

Wang, L. et al. MAML-en-LLM: mannequin agnostic meta-training of LLMs for improved in-context studying. Amazon Science Publications. (2024). https://www.amazon.science/publications/maml-en-llm-model-agnostic-meta-training-of-llms-for-improved-in-context-learning.

Brown, T. et al. Understanding model-agnostic meta-learning: MAML algorithms and functions. J. Mach. Learn. Res. 25 (1), 1–48 (2024).

Google Scholar 

Jin, W., He, Y., Song, Y., Zeng, D. & Gao, X. FedSlate: A federated deep reinforcement studying recommender system. IEEE Trans. Emerg. Top. Comput. Intell. 7 (6), 1653–1665. (2023).

Google Scholar 

Chen, Z., Wang, L., Li, X., Zhang, H. & Liu, Y. Reward steering for reinforcement studying duties based mostly on giant Language fashions: the LMGT framework. Knowl. Based Syst. 286, 111428. (2024).

Google Scholar 

Prasad, N., Cheng, L. F., Chivers, C., Draugelis, M. & Engelhardt, B. E. A model-based hybrid tender actor-critic deep reinforcement studying algorithm for optimum ventilator settings. Inf. Sci. 619, 447–462. (2023).

Google Scholar 

Liu, Y. et al. A latent batch-constrained deep reinforcement studying method for precision dosing medical choice assist. Knowl. Based Syst. 289, 111567. (2024).

Google Scholar 

Abadi, M. et al. Deep studying with differential privateness. In Proc. 2016 ACM SIGSAC Conf. Comput. Commun. Secur. 308-318 (2016).

Bonawitz, Ok. et al. Practical safe aggregation for privacy-preserving machine studying. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, 1175–1191. (2017). https://doi.org/10.1145/3133956.3133982.

Kairouz, P. et al. Advances and open issues in federated studying. Found. Trends Mach. Learn. 14 (1–2), 1–210. (2021).

Google Scholar 

Verniani, A. et al. Features of adaptive coaching algorithms for improved complicated talent acquisition. Front. Virtual Real. 5, 1322656. (2024).

Google Scholar 

Nottingham, Ok. et al. Skill set optimization: reinforcing Language mannequin conduct by way of transferable abilities. ArXiv Preprint arXiv. 240203244. (2024).

Johnson, M. et al. Training for elevated productiveness in 2024: personalised studying approaches. Learn. Dev. Q. 12 (3), 78–92 (2024).

Google Scholar 

Martinez, A. et al. Top 12 newest studying and growth tendencies in 2025. Int. J. Workplace Learn. 18 (1), 45–63 (2025).

Google Scholar 

Kim, H. et al. Skill switch analysis in office environments: methodologies and functions. Hum. Resour. Dev. Rev. 23 (4), 456–478 (2024).

Google Scholar 

Zhang, Q. et al. Review of computational fluid dynamics evaluation in biomimetic functions for underwater autos. Biomimetics 9 (2), 79. (2024).

Google Scholar 

Takagi, H. et al. A computational fluid dynamics evaluation of hydrodynamic drive appearing on a swimmer’s hand in a swimming competitors. J. Appl. Biomech. 39 (4), 245–256. (2023).

Google Scholar 

Drucker, E. G. & Lauder, G. V. Locomotion with versatile propulsors: II. Computational modeling of pectoral fin swimming in sunfish. Bioinspir. Biomim. 2 (4), S35–S48. (2007).

Google Scholar 

Hintjens, P. ZeroMQ: Messaging for Many Applications. O’Reilly Media. ISBN: 978-1449334062 (2013).

Tanenbaum, A. S. & Van Steen, M. Distributed Systems: Principles and Paradigms (third ed.). Pearson Education. ISBN: 978-1543057386 (2017).

Ota, Ok., Suzuki, R. & Aoki, T. Computational fluid dynamics of swimming microorganisms. J. Phys. Soc. Jpn. 92 (12), 121002. (2023).

Google Scholar 

Ortloff, C. R. Engineering and modeling of water circulation by way of computational fluid dynamics (CFD) and fashionable hydraulic evaluation strategies. Water 16 (21), 3086. (2024).

Google Scholar 

Zaïdi, H., Taiar, R., Fohanno, S. & Polidori, G. Unsteady computational fluid dynamics in entrance crawl swimming. J. Appl. Biomech. 33 (2), 161–165. (2017).

Google Scholar 

Bilinauskaite, M., Mantha, V. R., Rouboa, A. I., Ziliukas, P. & Silva, A. J. A computational fluid dynamics examine of propulsion as a result of orientation results of swimmer’s hand. J. Appl. Biomech. 30 (3), 343–351. (2014).

Google Scholar 

Rouboa, A., Monteiro, G., Silva, A., Gomes, R. & Barbosa, T. M. Computational fluid dynamics technique for the evaluation of the hydrodynamic efficiency in swimming. In Mass Transfer – Advancement in Process Modelling. IntechOpen. (2015). https://doi.org/10.5772/61821.

Liu, M. M. et al. Analysis of fluid drive and circulation fields throughout gliding in swimming utilizing smoothed particle hydrodynamics technique. Front. Bioeng. Biotechnol. 12, 1355617. (2024).

Google Scholar 

Peskin, C. S. Computational mannequin of the fluid dynamics of undulatory and flagellar swimming. Integr. Comp. Biol. 36 (6), 599–607. (1996).

Google Scholar 

Fefferman, C. L. Navier-Stokes equation existence and smoothness. Clay Mathematics Institute Millennium Problems. (2023). https://www.claymath.org/millennium/navier-stokes-equation/.

Sutton, R. S., Precup, D. & Singh, S. Between MDPs and semi-MDPs: A framework for Temporal abstraction in reinforcement studying. Artif. Intell. 112 (1–2), 181–211. (1999).

Google Scholar 

Bacon, P. L., Harb, J. & Precup, D. The option-critic structure. Proc. AAAI Conf. Artif. Intell. 31 (1), 1726–1734. (2017).

Google Scholar 

Thompson, R. & Johnson, Ok. Navier-Stokes equations in fluid dynamics: fashionable functions and computational strategies. J. Fluid Mech. 985, A23. (2024).

Google Scholar 

Ng, A. Y., Harada, D. & Russell, S. Policy invariance below reward transformations: Theory and software to reward shaping. In Proceedings of the sixteenth International Conference on Machine Learning, 278–287. (1999).

Sorg, J., Singh, S. & Lewis, R. L. Reward design by way of on-line gradient ascent. Adv. Neural. Inf. Process. Syst. 23, 2190–2198 (2010).

Google Scholar 

Anderson, P., Davis, M. & Wilson, C. Solving the Navier-Stokes equations in fluid mechanics: advances in computational approaches. Phys. Fluids. 36 (8), 081701. (2024).

Google Scholar 

NASA Glenn Research Center. Navier-Stokes equation: fundamentals and functions. NASA Technical Publication TP-2024-232156. (2024).

Lee, J. H. et al. Fundamentals of fluid dynamics: conservation legal guidelines and Navier-Stokes equations. Int. J. Eng. Educ. 39 (4), 892–905 (2023).

Google Scholar 

Brown, A. et al. Swimming coaching optimization via digital twin environments. Sports Technol. Perform. 8 (2), 123–138 (2024).

Google Scholar 

Chen, X. et al. Multi-agent reinforcement studying functions in aquatic sports activities coaching. IEEE Trans. Cybernetics. 54 (7), 3890–3904 (2024).

Google Scholar 

Rodriguez, M. et al. Performance evaluation of clever coaching programs in aggressive swimming. J. Sports Sci. 42 (12), 1156–1169 (2024).

Google Scholar 

Kumar, A. et al. Meta-learning methods for personalised athletic efficiency optimization. Mach. Learn. Sports. 6 (3), 201–218 (2024).

Google Scholar 

White, D. et al. Skill switch effectiveness in digital sports activities coaching environments. Comput. Hum. Behav. 153, 108142 (2024).

Google Scholar 

Adams, C. et al. Cross-population evaluation of swimming talent acquisition via meta-learning approaches. Appl. Psychol. Sports. 15 (4), 334–349 (2024).

Google Scholar 

Turner, O. et al. Personalized coaching influence evaluation in aggressive swimming. Int. J. Sports Physiol. Perform. 19 (8), 823–835 (2024).

Google Scholar 

Evans, A. et al. Cross-skill switch mechanisms in aquatic sports activities coaching. Sports Med. Open. 10, 87 (2024).

Google Scholar 

Collins, Ok. et al. Adaptive coaching system analysis for swimming efficiency enhancement. J. Biomech. 147, 111432 (2024).

Google Scholar 

Phillips, U. et al. Comprehensive framework validation for multi-agent swimming coaching programs. IEEE Trans. Syst. Man. Cybernetics: Syst. 54 (9), 5234–5247 (2024).

Google Scholar 

Edwards, E. et al. Innovation evaluation in clever sports activities coaching via digital twin integration. Comput. Educ. 206, 104912 (2024).

Google Scholar 

Owen, O. et al. T1, Walker, W1, Young, Y1, Allen, A2, computational complexity evaluation of multi-agent reinforcement studying in sports activities functions. J. Comput. Sci. 78, 102089. (2024).

Yang, Q., Liu, Y., Chen, T. & Tong, Y. Federated machine studying: idea and functions. ACM Trans. Intell. Syst. Technol. 10 (2), 1–19. (2019).

Google Scholar 

Lillicrap, T. P. et al. Continuous management with deep reinforcement studying. ArXiv Preprint arXiv. 150902971. (2019).

Silver, D. et al. A normal reinforcement studying algorithm that masters chess, shogi, and undergo self-play. Science 362 (6419), 1140–1144. (2018).

Google Scholar