Modelling and Data Analysis
2024. Vol. 14, no. 2, 7–22
doi:10.17759/mda.2024140201
ISSN: 2219-3758 / 2311-9454 (online)
Experience in Using the Transformer Network Architecture to Approximate Agent’s Policy in Reinforcement Learning
Abstract
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
Accepted:
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.)
References
- An outline of reinforcement learning // Arxiv URL: https://arxiv.org/pdf/2201.09746.pdf (circulation date: 02.01.2024).
- Proximal Policy Optimization Algorithms // Arxiv URL: https://arxiv.org/pdf/1707.06347.pdf (circulation date: 24.12.2023).
- Attention Is All You Need // Arxiv URL: https://arxiv.org/abs/1706.03762 (circulation date: 16.12.2023).
- Gymnasium URL: https://gymnasium.farama.org/ (circulation date: 10.12.2023).
- Stable Baselines Documentation // URL: https://buildmedia.readthedocs.org/media/pdf/stable-baselines/master/stable-baselines.pdf (circulation date: 08.12.2023).
- High-dimensional continuous control using generalized advantage estimation // Arxiv URL: https://arxiv.org/pdf/1506.02438.pdf (circulation date: 07.12.2023).
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