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DOI: 10.14569/IJACSA.2021.0120402
PDF

Autonomous Reusing Policy Selection using Spreading Activation Model in Deep Reinforcement Learning

Author 1: Yusaku Takakuwa
Author 2: Hitoshi Kono
Author 3: Hiromitsu Fujii
Author 4: Wen Wen
Author 5: Tsuyoshi Suzuki

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 4, 2021.

  • Abstract and Keywords
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Abstract: This paper describes a policy transfer method of a reinforcement learning agent based on the spreading activation model of cognitive psychology. This method has a prospect of increasing the possibility of policy reuse, adapting to multiple tasks, and assessing agent mechanism differences. In the existing methods, policies are evaluated and manually selected depending on the target–task. The proposed method generates a policy network that calculates the relevance between policies in order to select and transfer a specific policy that is presumed to be effective based on the current situation of the agent while learning. Using a policy network graph structure, the proposed method decides the most effective policy while repeating probabilistic selection, activation, and spread processing. In the experiment section, this study describes experiments conducted to evaluate usefulness, conditions of use, and the usable range of the proposed method. Tests using CartPole and MountainCar, which are classical reinforcement learning tasks, are described and transfer learning is compared between the proposed method and a Deep Q–Network without transfer. As the experimental results, usefulness was suggested in the transfer learning of the same task without manual compared with previous method with various conditions.

Keywords: Reinforcement learning; transfer learning; deep learning; cognitive psychology; spreading activation theory

Yusaku Takakuwa, Hitoshi Kono, Hiromitsu Fujii, Wen Wen and Tsuyoshi Suzuki, “Autonomous Reusing Policy Selection using Spreading Activation Model in Deep Reinforcement Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 12(4), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120402

@article{Takakuwa2021,
title = {Autonomous Reusing Policy Selection using Spreading Activation Model in Deep Reinforcement Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120402},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120402},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {4},
author = {Yusaku Takakuwa and Hitoshi Kono and Hiromitsu Fujii and Wen Wen and Tsuyoshi Suzuki}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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