Enhancing performance in special teams within the NFL through reinforcement learning: A data-driven approach

La tesis exploró la integración de la Ciencia de Datos y la Inteligencia Artificial en los equipos especiales de fútbol americano. Se utilizó el conjunto de datos NFL Big Data Bowl 2022 y la API de OpenAI Gym para crear un entorno de entrenamiento dinámico. Se entrenaron dos conjuntos de agentes que...

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Autores:
Tipo de recurso:
Fecha de publicación:
2023
Institución:
Universidad del Rosario
Repositorio:
Repositorio EdocUR - U. Rosario
Idioma:
eng
OAI Identifier:
oai:repository.urosario.edu.co:10336/40933
Acceso en línea:
https://doi.org/10.48713/10336_40933
https://repository.urosario.edu.co/handle/10336/40933
Palabra clave:
Aprendizaje reforzado
Ciencia de datos
Fútbol americano
Inteligencia Artificial
Procesos de toma de decisiones
Reinforcement learning
Data Science
American football
Artificial Intelligence
Decision-making processes
Rights
License
Attribution-NonCommercial-ShareAlike 4.0 International
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oai_identifier_str oai:repository.urosario.edu.co:10336/40933
network_acronym_str EDOCUR2
network_name_str Repositorio EdocUR - U. Rosario
repository_id_str
dc.title.none.fl_str_mv Enhancing performance in special teams within the NFL through reinforcement learning: A data-driven approach
dc.title.TranslatedTitle.none.fl_str_mv Mejorando el rendimiento en equipos especiales dentro de la NFL mediante el aprendizaje por refuerzo: Un enfoque basado en datos
title Enhancing performance in special teams within the NFL through reinforcement learning: A data-driven approach
spellingShingle Enhancing performance in special teams within the NFL through reinforcement learning: A data-driven approach
Aprendizaje reforzado
Ciencia de datos
Fútbol americano
Inteligencia Artificial
Procesos de toma de decisiones
Reinforcement learning
Data Science
American football
Artificial Intelligence
Decision-making processes
title_short Enhancing performance in special teams within the NFL through reinforcement learning: A data-driven approach
title_full Enhancing performance in special teams within the NFL through reinforcement learning: A data-driven approach
title_fullStr Enhancing performance in special teams within the NFL through reinforcement learning: A data-driven approach
title_full_unstemmed Enhancing performance in special teams within the NFL through reinforcement learning: A data-driven approach
title_sort Enhancing performance in special teams within the NFL through reinforcement learning: A data-driven approach
dc.contributor.advisor.none.fl_str_mv Caicedo Dorado, Alexander
dc.subject.none.fl_str_mv Aprendizaje reforzado
Ciencia de datos
Fútbol americano
Inteligencia Artificial
Procesos de toma de decisiones
topic Aprendizaje reforzado
Ciencia de datos
Fútbol americano
Inteligencia Artificial
Procesos de toma de decisiones
Reinforcement learning
Data Science
American football
Artificial Intelligence
Decision-making processes
dc.subject.keyword.none.fl_str_mv Reinforcement learning
Data Science
American football
Artificial Intelligence
Decision-making processes
description La tesis exploró la integración de la Ciencia de Datos y la Inteligencia Artificial en los equipos especiales de fútbol americano. Se utilizó el conjunto de datos NFL Big Data Bowl 2022 y la API de OpenAI Gym para crear un entorno de entrenamiento dinámico. Se entrenaron dos conjuntos de agentes que representaban diferentes posiciones dentro de los equipos especiales con el objetivo de aprender estrategias óptimas para alcanzar sus objetivos. El propósito era proporcionar información a los entrenadores, mejorar los procesos de toma de decisiones y aumentar el rendimiento en jugadas específicas.
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-09-07T16:54:59Z
dc.date.available.none.fl_str_mv 2023-09-07T16:54:59Z
dc.date.created.none.fl_str_mv 2023-07-25
dc.type.none.fl_str_mv bachelorThesis
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_7a1f
dc.type.document.none.fl_str_mv Trabajo de grado
dc.type.spa.none.fl_str_mv Trabajo de grado
dc.identifier.doi.none.fl_str_mv https://doi.org/10.48713/10336_40933
dc.identifier.uri.none.fl_str_mv https://repository.urosario.edu.co/handle/10336/40933
url https://doi.org/10.48713/10336_40933
https://repository.urosario.edu.co/handle/10336/40933
dc.language.iso.none.fl_str_mv eng
language eng
dc.rights.*.fl_str_mv Attribution-NonCommercial-ShareAlike 4.0 International
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dc.rights.acceso.none.fl_str_mv Abierto (Texto Completo)
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rights_invalid_str_mv Attribution-NonCommercial-ShareAlike 4.0 International
Abierto (Texto Completo)
http://creativecommons.org/licenses/by-nc-sa/4.0/
http://purl.org/coar/access_right/c_abf2
dc.format.extent.none.fl_str_mv 69 pp
dc.format.mimetype.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidad del Rosario
dc.publisher.department.none.fl_str_mv Escuela de Ingeniería, Ciencia y Tecnología
dc.publisher.program.none.fl_str_mv Programa de Matemáticas Aplicadas y Ciencias de la Computación - MACC
publisher.none.fl_str_mv Universidad del Rosario
institution Universidad del Rosario
dc.source.bibliographicCitation.none.fl_str_mv N. Chmait and H. Westerbeek, “Artificial intelligence and machine learning in sport research: An introduction for non-data scientists,” Frontiers in Sports and Active Living, p. 363, 2021.
U. Brefeld, J. Davis, M. Lames, and J. J. Little, “Machine learning in sports (dagstuhl seminar 21411),” in Dagstuhl Reports, Schloss Dagstuhl-Leibniz-Zentrum für Informatik, vol. 11, 2022.
J. McCarthy, “What is artificial intelligence,” 2007.
I. El Naqa and M. J. Murphy, What is machine learning? Springer, 2015
T. O. Ayodele, “Types of machine learning algorithms,” New advances in machine learning, vol. 3, pp. 19–48, 2010
S. S. Mousavi, M. Schukat, and E. Howley, “Deep reinforcement learning: An overview,” in Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016: Volume 2, Springer, 2018, pp. 426–440.
M. A. Wiering et al., “Reinforcement learning in dynamic environments using instantiated information,” in Machine Learning: Proceedings of the Eighteenth International Conference (ICML2001), 2001, pp. 585–592
X. Lei, Z. Zhang, and P. Dong, “Dynamic path planning of unknown environment based on deep reinforcement learning,” Journal of Robotics, vol. 2018, 2018.
R. Graham, H. McCabe, and S. Sheridan, “Neural networks for real-time pathfinding in computer games,” ITB J, vol. 5, no. 1, p. 21, 2004.
M. Sinkar, M. Izhan, S. Nimkar, and S. Kurhade, “Multi-agent path finding using dynamic distributed deep learning model,” in 2021 International Conference on Communication information and Computing Technology (ICCICT), IEEE, 2021, pp. 1–6.
S. Saeedvand, S. N. Razavi, and F. Ansaroudi, “Path-finding in multi-agent, unexplored and dynamic military environment using genetic algorithm,” Journal of World’s Electrical Engineering and Technology, vol. 2322, p. 5114, 2015.
D. Jugan and D. T. Ahmed, “A decision-making and actions framework for ball carriers in american football.,” in MAICS, 2015, pp. 109–116.
O. Baykal and F. N. Alpaslan, “Supervised learning in football game environments using artificial neural networks,” in 2018 3rd International Conference on Computer Science and Engineering (UBMK), IEEE, 2018, pp. 110–115.
S. Omidshafiei, D. Hennes, M. Garnelo, et al., “Multiagent off-screen behavior prediction in football,” Scientific reports, vol. 12, no. 1, p. 8638, 2022.
A. Krogh, “What are artificial neural networks?” Nature biotechnology, vol. 26, no. 2, pp. 195–197, 2008.
B. M. Wilamowski, “Neural network architectures and learning,” in IEEE International Conference on Industrial Technology, 2003, IEEE, vol. 1, 2003, TU1– T12.
A. Drewek-Ossowicka, M. Pietrołaj, and J. Rumi ´nski, “A survey of neural networks usage for intrusion detection systems,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 497–514, 2021.
K. G. Kim, “Book review: Deep learning,” Healthcare informatics research, vol. 22, no. 4, pp. 351–354, 2016.
M. Reza, “Galaxy morphology classification using automated machine learning,” Astronomy and Computing, vol. 37, p. 100 492, 2021.
M. Z. Alom, T. M. Taha, C. Yakopcic, et al., “A state-of-the-art survey on deep learning theory and architectures,” electronics, vol. 8, no. 3, p. 292, 2019
B. Kumaraswamy, “Neural networks for data classification,” in Artificial intelligence in data mining, Elsevier, 2021, pp. 109–131.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature, vol. 521, no. 7553, pp. 436–444, 2015.
Y. Li, “Deep reinforcement learning: An overview,” arXiv preprint arXiv:1701.07274, 2017.
P.Dell’Aversana, “Reinforcement learning in optimization problems. applications to geophysical data inversion,” AIMS Geosci, vol. 8, pp. 488–502, 2022.
R. S. Sutton, A. G. Barto, et al., “Reinforcement learning,” Journal of Cognitive Neuroscience, vol. 11, no. 1, pp. 126–134, 1999.
S. Porwal and D. Vora, “A comparative analysis of data cleaning approaches to dirty data,” International Journal of Computer Applications, vol. 62, no. 17, pp. 30– 34, 2013
E. Rahm, H. H. Do, et al., “Data cleaning: Problems and current approaches,” IEEE Data Eng. Bull., vol. 23, no. 4, pp. 3–13, 2000.
L. Sun, J. Zhai, and W. Qin, “Crowd navigation in an unknown and dynamic environment based on deep reinforcement learning,” IEEE Access, vol. 7, pp. 109 544– 109 554, 2019
M. L. Puterman, “Markov decision processes,” Handbooks in operations research and management science, vol. 2, pp. 331–434, 1990.
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv preprint arXiv:1707.06347, 2017.
Y. Liu, D. Zhang, and H. B. Gooi, “Data-driven decision-making strategies for electricity retailers: A deep reinforcement learning approach,” CSEE Journal of Power and Energy Systems, vol. 7, no. 2, pp. 358–367, 2020
Y. Duan, X. Chen, R. Houthooft, J. Schulman, and P. Abbeel, “Benchmarking deep reinforcement learning for continuous control,” in International conference on machine learning, PMLR, 2016, pp. 1329–1338.
L. Espeholt, H. Soyer, R. Munos, et al., “Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures,” in International conference on machine learning, PMLR, 2018, pp. 1407–1416
A. Tato and R. Nkambou, “Improving adam optimizer,” 2018.
J. Schmidt-Hieber, “Nonparametric regression using deep neural networks with relu activation function,” 2020
L. Pauly, D Hogg, R Fuentes, and H Peel, “Deeper networks for pavement crack detection,” in Proceedings of the 34th ISARC, IAARC, 2017, pp. 479–485.
M. Abadi, P. Barham, J. Chen, et al., “Tensorflow: A system for large-scale machine learning.,” in Osdi, Savannah, GA, USA, vol. 16, 2016, pp. 265–28
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spelling Caicedo Dorado, Alexander654b415e-488b-4204-9e90-1846db9bc0a6-1Alvarez Barbosa, SantiagoProfesional en Matemáticas Aplicadas y Ciencias de la ComputaciónPregradoFull time3dd5264a-80e2-4004-8241-b1f634021b78-12023-09-07T16:54:59Z2023-09-07T16:54:59Z2023-07-25La tesis exploró la integración de la Ciencia de Datos y la Inteligencia Artificial en los equipos especiales de fútbol americano. Se utilizó el conjunto de datos NFL Big Data Bowl 2022 y la API de OpenAI Gym para crear un entorno de entrenamiento dinámico. Se entrenaron dos conjuntos de agentes que representaban diferentes posiciones dentro de los equipos especiales con el objetivo de aprender estrategias óptimas para alcanzar sus objetivos. El propósito era proporcionar información a los entrenadores, mejorar los procesos de toma de decisiones y aumentar el rendimiento en jugadas específicas.The thesis explored the integration of Data Science and Artificial Intelligence in American football special teams. The NFL Big Data Bowl 2022 dataset and the OpenAI Gym API were used to create a dynamic training environment. Two sets of agents representing different positions within special teams were trained with the aim of learning optimal strategies to achieve their objectives. The goal was to provide information to the coaches, enhance decision-making processes, and improve performance in specific plays.69 ppapplication/pdfhttps://doi.org/10.48713/10336_40933 https://repository.urosario.edu.co/handle/10336/40933engUniversidad del RosarioEscuela de Ingeniería, Ciencia y TecnologíaPrograma de Matemáticas Aplicadas y Ciencias de la Computación - MACCAttribution-NonCommercial-ShareAlike 4.0 InternationalAbierto (Texto Completo)http://creativecommons.org/licenses/by-nc-sa/4.0/http://purl.org/coar/access_right/c_abf2N. Chmait and H. Westerbeek, “Artificial intelligence and machine learning in sport research: An introduction for non-data scientists,” Frontiers in Sports and Active Living, p. 363, 2021.U. Brefeld, J. Davis, M. Lames, and J. J. Little, “Machine learning in sports (dagstuhl seminar 21411),” in Dagstuhl Reports, Schloss Dagstuhl-Leibniz-Zentrum für Informatik, vol. 11, 2022.J. McCarthy, “What is artificial intelligence,” 2007.I. El Naqa and M. J. Murphy, What is machine learning? Springer, 2015T. O. Ayodele, “Types of machine learning algorithms,” New advances in machine learning, vol. 3, pp. 19–48, 2010S. S. Mousavi, M. Schukat, and E. Howley, “Deep reinforcement learning: An overview,” in Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016: Volume 2, Springer, 2018, pp. 426–440.M. A. Wiering et al., “Reinforcement learning in dynamic environments using instantiated information,” in Machine Learning: Proceedings of the Eighteenth International Conference (ICML2001), 2001, pp. 585–592X. Lei, Z. Zhang, and P. Dong, “Dynamic path planning of unknown environment based on deep reinforcement learning,” Journal of Robotics, vol. 2018, 2018.R. Graham, H. McCabe, and S. Sheridan, “Neural networks for real-time pathfinding in computer games,” ITB J, vol. 5, no. 1, p. 21, 2004.M. Sinkar, M. Izhan, S. Nimkar, and S. Kurhade, “Multi-agent path finding using dynamic distributed deep learning model,” in 2021 International Conference on Communication information and Computing Technology (ICCICT), IEEE, 2021, pp. 1–6.S. Saeedvand, S. N. Razavi, and F. Ansaroudi, “Path-finding in multi-agent, unexplored and dynamic military environment using genetic algorithm,” Journal of World’s Electrical Engineering and Technology, vol. 2322, p. 5114, 2015.D. Jugan and D. T. Ahmed, “A decision-making and actions framework for ball carriers in american football.,” in MAICS, 2015, pp. 109–116.O. Baykal and F. N. Alpaslan, “Supervised learning in football game environments using artificial neural networks,” in 2018 3rd International Conference on Computer Science and Engineering (UBMK), IEEE, 2018, pp. 110–115.S. Omidshafiei, D. Hennes, M. Garnelo, et al., “Multiagent off-screen behavior prediction in football,” Scientific reports, vol. 12, no. 1, p. 8638, 2022.A. Krogh, “What are artificial neural networks?” Nature biotechnology, vol. 26, no. 2, pp. 195–197, 2008.B. M. Wilamowski, “Neural network architectures and learning,” in IEEE International Conference on Industrial Technology, 2003, IEEE, vol. 1, 2003, TU1– T12.A. Drewek-Ossowicka, M. Pietrołaj, and J. Rumi ´nski, “A survey of neural networks usage for intrusion detection systems,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 497–514, 2021.K. G. Kim, “Book review: Deep learning,” Healthcare informatics research, vol. 22, no. 4, pp. 351–354, 2016.M. Reza, “Galaxy morphology classification using automated machine learning,” Astronomy and Computing, vol. 37, p. 100 492, 2021.M. Z. Alom, T. M. Taha, C. Yakopcic, et al., “A state-of-the-art survey on deep learning theory and architectures,” electronics, vol. 8, no. 3, p. 292, 2019B. Kumaraswamy, “Neural networks for data classification,” in Artificial intelligence in data mining, Elsevier, 2021, pp. 109–131.Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature, vol. 521, no. 7553, pp. 436–444, 2015.Y. Li, “Deep reinforcement learning: An overview,” arXiv preprint arXiv:1701.07274, 2017.P.Dell’Aversana, “Reinforcement learning in optimization problems. applications to geophysical data inversion,” AIMS Geosci, vol. 8, pp. 488–502, 2022.R. S. Sutton, A. G. Barto, et al., “Reinforcement learning,” Journal of Cognitive Neuroscience, vol. 11, no. 1, pp. 126–134, 1999.S. Porwal and D. Vora, “A comparative analysis of data cleaning approaches to dirty data,” International Journal of Computer Applications, vol. 62, no. 17, pp. 30– 34, 2013E. Rahm, H. H. Do, et al., “Data cleaning: Problems and current approaches,” IEEE Data Eng. Bull., vol. 23, no. 4, pp. 3–13, 2000.L. Sun, J. Zhai, and W. Qin, “Crowd navigation in an unknown and dynamic environment based on deep reinforcement learning,” IEEE Access, vol. 7, pp. 109 544– 109 554, 2019M. L. Puterman, “Markov decision processes,” Handbooks in operations research and management science, vol. 2, pp. 331–434, 1990.J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv preprint arXiv:1707.06347, 2017.Y. Liu, D. Zhang, and H. B. Gooi, “Data-driven decision-making strategies for electricity retailers: A deep reinforcement learning approach,” CSEE Journal of Power and Energy Systems, vol. 7, no. 2, pp. 358–367, 2020Y. Duan, X. Chen, R. Houthooft, J. Schulman, and P. Abbeel, “Benchmarking deep reinforcement learning for continuous control,” in International conference on machine learning, PMLR, 2016, pp. 1329–1338.L. Espeholt, H. Soyer, R. Munos, et al., “Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures,” in International conference on machine learning, PMLR, 2018, pp. 1407–1416A. Tato and R. Nkambou, “Improving adam optimizer,” 2018.J. Schmidt-Hieber, “Nonparametric regression using deep neural networks with relu activation function,” 2020L. Pauly, D Hogg, R Fuentes, and H Peel, “Deeper networks for pavement crack detection,” in Proceedings of the 34th ISARC, IAARC, 2017, pp. 479–485.M. Abadi, P. Barham, J. 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