Article
Version 1
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On Renormalization Group Based Deep Q-Network
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Version 1
: Received: 11 July 2024 / Approved: 11 July 2024 / Online: 11 July 2024 (13:03:43 CEST)
How to cite: Garayev, G.; Alili, A. On Renormalization Group Based Deep Q-Network. Preprints 2024, 2024070953. https://doi.org/10.20944/preprints202407.0953.v1 Garayev, G.; Alili, A. On Renormalization Group Based Deep Q-Network. Preprints 2024, 2024070953. https://doi.org/10.20944/preprints202407.0953.v1
Abstract
In This paper we introduce the integration of Renormalization Group (RG) methods with Deep Q-Networks (DQNs) to improve reinforcement learning in high-dimensional state spaces. RG methods provide multi-scale analysis, enhancing state representation, learning stability, and exploration. The proposed RG-DQN algorithm uses hierarchical Q-value estimation and multi-scale representations, demonstrating superior performance on synthetic genomic data compared to traditional DQNs.}
Keywords
DQN; renormalization group; AI; loss functions
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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