Experience in Using the Transformer Network Architecture to Approximate Agent’s Policy in Reinforcement Learning



This paper discusses the basics of the deep reinforcement learning algorithm and the use of neural networks to approximate the agent’s policy. The comparison of using a fully connected neural network and a transformer network in the reinforcement learning algorithm is considered.

General Information

Keywords: artificial intelligence, machine learning, deep reinforcement learning, Markov decision processes, transformer, optimization

Journal rubric: Data Analysis

Article type: scientific article

DOI: https://doi.org/10.17759/mda.2024140201

Received: 03.06.2024


For citation: Novikov N.P., Vinogradov V.I. Experience in Using the Transformer Network Architecture to Approximate Agent’s Policy in Reinforcement Learning. Modelirovanie i analiz dannikh = Modelling and Data Analysis, 2024. Vol. 14, no. 2, pp. 7–22. DOI: 10.17759/mda.2024140201. (In Russ., аbstr. in Engl.)


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Information About the Authors

Nikita P. Novikov, master's student, Institute of Computer Science and Applied Mathematics, Moscow Aviation Institute (National Research University) (MAI), Moscow, Russia, e-mail: rtyderson@gmail.com

Vladimir I. Vinogradov, PhD in Physics and Matematics, Associate Professor, Department of Mathematical Cybernetics, Moscow Aviation Institute (National Research University), Moscow, Russia, ORCID: https://orcid.org/0000-0003-3773-9653, e-mail: vvinogradov@inbox.ru



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