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Potentials of reinforcement learning in contemporary scenarios

НазваPotentials of reinforcement learning in contemporary scenarios
Назва англійськоюPotentials of reinforcement learning in contemporary scenarios
АвториAbubakar Sadiq Abdulhameed, Serhii Lupenko
ПринадлежністьTernopil Ivan Puluj National Technical University, Ternopil, Ukraine
Бібліографічний описPotentials of reinforcement learning in contemporary scenarios / Abubakar Sadiq Abdulhameed, Serhii Lupenko // Scientific Journal of TNTU. — Tern.: TNTU, 2022. — Vol 106. — No 2. — P. 92–100.
Bibliographic description:Abubakar Sadiq Abdulhameed, Lupenko S. (2022) Potentials of reinforcement learning in contemporary scenarios. Scientific Journal of TNTU (Tern.), vol 106, no 2, pp. 92–100.
DOI: https://doi.org/10.33108/visnyk_tntu2022.02.092
УДК

004

Ключові слова

Reinforcement learning, model-based learning, model-free learning, algorithms, medical image processing, deep reinforcement learning.

This paper reviews the present applications of reinforcement learning in five major spheres including mobile autonomy, industrial autonomy, finance and trading, and gaming. The application of reinforcement learning in real time cannot be overstated, it encompasses areas far beyond the scope of this paper, including but not limited to medicine, health care, natural language processing, robotics and e-commerce. Contemporary reinforcement learning research teams have made remarkable progress in games and comparatively less in the medical field. Most recent implementations of reinforcement learning are focused on model-free learning algorithms as they are relatively easier to implement. This paper seeks to present model-based reinforcement learning notions, and articulate how model-based learning can be efficient in contemporary scenarios. Model based reinforcement learning is a fundamental approach to sequential decision making, it refers to learning optimal behavior indirectly by learning a model of the environment, from taking actions and observing the outcomes that include the subsequent sate and the instant reward. Many other spheres of reinforcement learning have a connection to model-based reinforcement learning. The findings of this paper could have both academic and industrial ramifications, enabling individual.

ISSN:2522-4433
Перелік літератури
  1. Mnih V., Kavukcuoglu K., Silver D., Rusu A. A., Veness J., Bellemare M. G., et al. Human-level control through deep reinforcement learning. Nature. 2015 Feb. 518 (7540). Р. 529–33.
  2. Volodymyr Mnih, Koray Kavukcuoglu, et al. Playing Atari with deep Reinforcement Learning. Cornell University, Dec 2013. 
  3. Micheal Painter, Luke Johnston. Mastering the game of Go from scratch. Stanford University.
  4. Silver D., Hubert T., Schrittwieser J., Antonoglou I., Lai M., Guez A., et al. Mastering Chess and Shogi by Self-Play with a General Rein-forcement Learning Algorithm. arXiv. 2017. 1712.01815.
  5. Hu J., Niu H., Carrasco J., Lennox B., Arvin F. (2020). “Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning”. IEEE Transactions on Vehicular Technology. 69 (12): 14413–14423. Doi:10.1109/TVT.2020.3034800. S2CID 228989788.
  6. Sutton, R. S., Barto A. G., Reinforcement Learning: An Introduction; A Bradford Book: Cambridge, MA, USA, 2018. 
  7. Zhang H., Yu T., Taxonomy of Reinforcement Learning Algorithms. In Deep Reinforcement Learning: Fundamentals, Research and Applications; Dong, H., Ding, Z., Zhang, S., Eds.; Springer: Singapore, 2020; P. 125–133. 
  8. Rummery G. A., Niranjan M. On-line Q-Learning Using Connectionist Systems; University of Cambridge, Department of Engineering: Cambridge, UK, 1994; Volume 37. 
  9. Deisenroth M. P., Neumann G., Peters, J. A survey on policy search for robotics. Found. Trends® Robot. 2013. 2. Р. 388–403. 
  10. Neziha Akalin and Amy Loutfi. Reinforcement learning approaches in social robotics. MDPI: 11, February 2021. URL: https://www.mdpi.com/1424-8220/21/4/1292/htm.
  11. Thomas M. Moerland et al. Model-based Reinforcement Learning: A survey version 3 Arrive, Cornell University: 25 Feb, 2021. URL: https://arxiv.org/abs/2006.16712.
  12. Micheal Janner. Model-bassed reinforcement learning: Theory and Practice. Berkeley Artificial Intelligence Research: Dec 12, 2019. URL: https://bair.berkeley.edu/blog/2019/12/12/mbpo/.
  13. Tingwu Wang, et al. Bench Marking Model-Based Reinforcement Learning. Axiv, Cornell University: 3, Jul, 2019. URL: https://arxiv.org/abs/1907.02057.
  14. Antonio Gull, Sujit Pal. Convolutional Neural Network with Reinforcement Learning. Packt: April 6, 2017. URL: https://hub.packtpub.com/convolutional-neural-networks-reinforcement-learning/.
  15. Phung V. H., Rhee E. J. A Deep Learning Approach for Classification of Cloud Image Patches on Small Datasets. J. Inf. Commun. Converg. Eng. 2018. 16. Р. 173–178. Doi:10.6109/jicce.2018.16.3.173.
  16. Derrick Mwiti. 10 Real-Life Applications of Reinforcement Learning. Neptune; Nov, 2021. URL: https://neptune.ai/blog/reinforcement-learning-applications.
  17. Dmitry Kalashnikov, Alex Irpan. QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation (v3). Cornell University. Nov, 2018.
  18. Anders Jonsson. Deep Reinforcement Learning in Medicine. Universitat Pompeu Fabra, Barcelona Spain. October 12, 2018. URL: https://www.karger.com/Article/Fulltext/492670.
  19. Pineau J., Guez A., Vincent R., Panuccio G., Avili M. Treating epilepsy via adaptive neurostimulation: a reinforcement learning approach. Int J Neural Syst. 2009 August, 19 (4): 227-40.
  20. Zhao Y., Zeng D., Socinski M. A., Kosorok M. R.. Reinforcement learning strategies for clinical trails in non small cell lung cancer. Biometrics 2011 Dec; 67(4): 1422-33.
  21. Liu Y., Logan B., Liu N., Xu Z., Tang J., Wang Y. Deep reinforcement learning for dynamic treatment regimes on medical registry data. 2017 IEEE International Conference on Health Informatics (ICHI); 2017 Aug. P. 380-385.
References:
  1. Mnih V., Kavukcuoglu K., Silver D., Rusu A. A., Veness J., Bellemare M. G., et al. Human-level control through deep reinforcement learning. Nature. 2015 Feb. 518 (7540). Р. 529–33.
  2. Volodymyr Mnih, Koray Kavukcuoglu, et al. Playing Atari with deep Reinforcement Learning. Cornell University, Dec 2013. 
  3. Micheal Painter, Luke Johnston. Mastering the game of Go from scratch. Stanford University.
  4. Silver D., Hubert T., Schrittwieser J., Antonoglou I., Lai M., Guez A., et al. Mastering Chess and Shogi by Self-Play with a General Rein-forcement Learning Algorithm. arXiv. 2017. 1712.01815.
  5. Hu J., Niu H., Carrasco J., Lennox B., Arvin F. (2020). “Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning”. IEEE Transactions on Vehicular Technology. 69 (12): 14413–14423. Doi:10.1109/TVT.2020.3034800. S2CID 228989788.
  6. Sutton, R. S., Barto A. G., Reinforcement Learning: An Introduction; A Bradford Book: Cambridge, MA, USA, 2018. 
  7. Zhang H., Yu T., Taxonomy of Reinforcement Learning Algorithms. In Deep Reinforcement Learning: Fundamentals, Research and Applications; Dong, H., Ding, Z., Zhang, S., Eds.; Springer: Singapore, 2020; P. 125–133. 
  8. Rummery G. A., Niranjan M. On-line Q-Learning Using Connectionist Systems; University of Cambridge, Department of Engineering: Cambridge, UK, 1994; Volume 37. 
  9. Deisenroth M. P., Neumann G., Peters, J. A survey on policy search for robotics. Found. Trends® Robot. 2013. 2. Р. 388–403. 
  10. Neziha Akalin and Amy Loutfi. Reinforcement learning approaches in social robotics. MDPI: 11, February 2021. URL: https://www.mdpi.com/1424-8220/21/4/1292/htm.
  11. Thomas M. Moerland et al. Model-based Reinforcement Learning: A survey version 3 Arrive, Cornell University: 25 Feb, 2021. URL: https://arxiv.org/abs/2006.16712.
  12. Micheal Janner. Model-bassed reinforcement learning: Theory and Practice. Berkeley Artificial Intelligence Research: Dec 12, 2019. URL: https://bair.berkeley.edu/blog/2019/12/12/mbpo/.
  13. Tingwu Wang, et al. Bench Marking Model-Based Reinforcement Learning. Axiv, Cornell University: 3, Jul, 2019. URL: https://arxiv.org/abs/1907.02057.
  14. Antonio Gull, Sujit Pal. Convolutional Neural Network with Reinforcement Learning. Packt: April 6, 2017. URL: https://hub.packtpub.com/convolutional-neural-networks-reinforcement-learning/.
  15. Phung V. H., Rhee E. J. A Deep Learning Approach for Classification of Cloud Image Patches on Small Datasets. J. Inf. Commun. Converg. Eng. 2018. 16. Р. 173–178. Doi:10.6109/jicce.2018.16.3.173.
  16. Derrick Mwiti. 10 Real-Life Applications of Reinforcement Learning. Neptune; Nov, 2021. URL: https://neptune.ai/blog/reinforcement-learning-applications.
  17. Dmitry Kalashnikov, Alex Irpan. QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation (v3). Cornell University. Nov, 2018.
  18. Anders Jonsson. Deep Reinforcement Learning in Medicine. Universitat Pompeu Fabra, Barcelona Spain. October 12, 2018. URL: https://www.karger.com/Article/Fulltext/492670.
  19. Pineau J., Guez A., Vincent R., Panuccio G., Avili M. Treating epilepsy via adaptive neurostimulation: a reinforcement learning approach. Int J Neural Syst. 2009 August, 19 (4): 227-40.
  20. Zhao Y., Zeng D., Socinski M. A., Kosorok M. R.. Reinforcement learning strategies for clinical trails in non small cell lung cancer. Biometrics 2011 Dec; 67(4): 1422-33.
  21. Liu Y., Logan B., Liu N., Xu Z., Tang J., Wang Y. Deep reinforcement learning for dynamic treatment regimes on medical registry data. 2017 IEEE International Conference on Health Informatics (ICHI); 2017 Aug. P. 380-385.
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