Detection of moving fish schools using reinforcement learning technique

Mehmet Yaşar Bayraktar

doi:10.12714/egejfas.42.1.03

EN TR

Detection of moving fish schools using reinforcement learning technique

Abstract

In this study, it is aimed to contribute to the fishing sector by determining the locations of moving fish schools. With the Q-Learning algorithm, areas where fish schools are frequently seen were marked and autonomous ships were able to reach these areas faster. With the Q-Learning algorithm, one of the machine learning techniques, areas where fish schools are abundant were determined and reward and penalty points were given to each region. In addition, the fish density matrix of the region was extracted thanks to the autonomous systems. Moreover, the algorithm can be automatically updated according to fish species and fishing bans. A different Q-Gain matrix was kept for each fish species to be caught, allowing autonomous ships to move according to the gain matrix. In short, high gains were achieved in terms of time and travel costs in finding or following fish schools by recognizing the region by autonomous ships.

Keywords

Etik Beyan

For this type of study, formal consent is not required.

Kaynakça

Angiuli, A., Fouque, J.P., & Laurière, M. (2022). Unified reinforcement Q-learning for mean field game and control problems. Mathematics of Control, Signals, and Systems, 34(2), 217 271. https://doi.org/10.1007/s00498-021-00310-1
Aydındağ Bayrak, E., Kırcı, P., Ensari, T., Seven, E., & Dağtekin, M. (2022). Diagnosing breast cancer using machine learning methods. (in Turkish with English abstract) Journal of Intelligent Systems: Theory and Applications, 5(1), 35-41. https://doi.org/10.38016/jista.966517
Barto, A.G., Bradtke, S.J., & Singh, S.P. (1995). Learning to act using real-time dynamic programming. Artificial Intelligence, 72(1-2), 81-138. https://doi.org/10.1016/0004-3702(94)00011-O
Chapman, D., & Kaelbling, L.P. (1991). Input generalization in delayed reinforcement learning: An algorithm and performance comparisons. Proceedings of the 1991. International Joint Conference on Artificial Intelligence, 726–731 pp., Sydney, Australia.
Christiano, P.F., Leike, J., Brown, T., Martic, M., Legg, S., & Amodei, D. (2017). Deep reinforcement learning from human preferences. Advances in Neural Information Processing Systems, 30. http://dx.doi.org/10.48550/arXiv.1706.03741
D'Eramo, C., Cini, A., Nuara, A., Pirotta, M., Alippi, C., Peters, J., & Restelli, M. (2021). Gaussian approximation for bias reduction in Q-learning. Journal of Machine Learning Research, 22(277), 1-51.
D'Eramo, C., Nuara, A., Pirotta, M., & Restelli, M. (2017). Estimating the maximum expected value in continuous reinforcement learning problems. In Proceedings of the AAAI Conference on Artificial Intelligence, 31(1), 1846-1846.
Dayan, P. (1993). Improving generalization for temporal difference learning: The successor representation. Neural Computation, 5(4), 613-624. https://doi.org/10.1162/neco.1993.5.4.613

Devlin, S., Yliniemi, L., Kudenko, D., & Tumer, K. (2014). Potential-based difference rewards for multiagent reinforcement learning. In Proceedings of the 2014 International Conference on Autonomous Agents and Multi-Agent Systems, 165-172 pp.
Elallid, B.B., Benamar, N., Hafid, A.S., Rachidi, T., & Mrani, N. (2022). A comprehensive survey on the application of deep and reinforcement learning approaches in autonomous driving. Journal of King Saud University-Computer and Information Sciences, 34(9), 7366-7390. https://doi.org/10.1016/j.jksuci.2022.03.013
Everitt, T., Krakovna, V., Orseau, L., Hutter, M., & Legg, S. (2017). Reinforcement learning with a corrupted reward channel. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17), 4705-4713.
Gümüş, E. (2016). Q-Learning Algoritması ile Labirentte Yol Bulmak. 7(2), 1–23. https://github.com/emrahgumus/java-q-learning-labirent.git(Erişim Tarihi: 10.09.2024)
Jogunola, O., Adebisi, B., Ikpehai, A., Popoola, S.I., Gui, G., Gačanin, H., & Ci, S. (2021). Consensus algorithms and deep reinforcement learning in energy market: A review. IEEE Internet of Things Journal, 8(6), 4211-4227. https://doi.org/10.1109/JIOT.2020.3032162
Jones, G.L., & Qin, Q. (2022). Markov chain Monte Carlo in practice. Annual Review of Statistics and Its Application, 9(1), 557-578. https://doi.org/10.1146/annurev-statistics-040220-090158
Jordan, M.I., & Mitchell, T.M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260. https://doi.org/10.1126/science.aaa8415
Kober, J., Bagnell, J.A., & Peters, J. (2013). Reinforcement learning in robotics: A survey. The International Journal of Robotics Research, 32(11), 1238-1274. https://doi.org/10.1177/0278364913495721
Nykjaer, K. (2022). Q Learning Library. https://kunuk.wordpress.com/2012/01/14/q-learning-library-example-with-csharp/ (Erişim Tarihi: 11.09.2024)
Lin, L.J. (1992). Self-improving reactive agents based on reinforcement learning, planning and teaching. Machine Learning, 8, 293-321. https://doi.org/10.1007/BF00992699
Liu, H., Bi, W., Teo, K.L., & Liu, N. (2019). Dynamic optimal decision making for manufacturers with limited attention based on sparse dynamic programming. Journal of Industrial & Management Optimization, 15(2). https://doi.org/10.3934/jimo.2018050
Meng, T.L., & Khushi, M. (2019). Reinforcement learning in financial markets. Data, 4(3), 110. https://doi.org/10.3390/data4030110
Pandey, P., Pandey, D., & Kumar, S. (2010). Reinforcement learning by comparing immediate reward. International Journal of Computer Science and Information Security, 8(5), 1009.2566. https://doi.org/10.48550/arXiv.1009.2566
Parisotto, E. (2021). Meta reinforcement learning through memory. Doctoral dissertation, Pittsburgh, Carnegie Mellon University.
Van Seijen, H., Fatemi, M., Romoff, J., Laroche, R., Barnes, T., & Tsang, J. (2017). Hybrid reward architecture for reinforcement learning. Advances in Neural Information Processing Systems, 30. ISBN: 9781510860964.
Wang, J., Liu, Y., & Li, B. (2020, April). Reinforcement learning with perturbed rewards. In Proceedings of the AAAI conference on artificial intelligence, 34(04), 6202-6209. https://doi.org/10.1609/aaai.v34i04.6086
Watkins, C.J.C.H. (1989). Learning from delayed rewards. Doctoral dissertation, King's College, London, UK.
Watkins, C.J.C.H., & Dayan, P. (1992). Q-learning. Machine Learning, 8, 279-292. https://doi.org/10.1007/BF00992698

Ayrıntılar

Birincil Dil

İngilizce

Konular

Balıkçılık Yönetimi

Bölüm

Araştırma Makalesi

Yazarlar

Mehmet Yaşar Bayraktar ^*
0000-0003-3182-120X
Türkiye

Yayımlanma Tarihi

8 Mart 2025

Gönderilme Tarihi

5 Eylül 2024

Kabul Tarihi

15 Ocak 2025

Yayımlandığı Sayı

Yıl 2025 Cilt: 42 Sayı: 1

DOI

https://doi.org/10.12714/egejfas.42.1.03

IZ

https://izlik.org/JA69SS42CC

Kaynak Göster

RIS / Bibtex

APA

Bayraktar, M. Y. (2025). Detection of moving fish schools using reinforcement learning technique. Ege Journal of Fisheries and Aquatic Sciences, 42(1), 21-26. https://doi.org/10.12714/egejfas.42.1.03

Detection of moving fish schools using reinforcement learning technique

Detection of moving fish schools using reinforcement learning technique

Abstract

Keywords

Takviyeli öğrenme tekniği kullanarak haraketli balık sürülerinin tespiti

Öz

Anahtar Kelimeler

Etik Beyan

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster