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DOI: 10.14569/IJACSA.2019.0101202
PDF

Activation and Spreading Sequence for Spreading Activation Policy Selection Method in Transfer Reinforcement Learning

Author 1: Hitoshi Kono
Author 2: Ren Katayama
Author 3: Yusaku Takakuwa
Author 4: Wen Wen
Author 5: Tsuyoshi Suzuki

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 12, 2019.

  • Abstract and Keywords
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Abstract: This paper proposes an automatic policy selection method using spreading activation theory based on psychological theory for transfer learning in reinforcement learning. Intel-ligent robot systems have recently been studied for practical applications such as home robot, communication robot, and warehouse robot. Learning algorithms are key to building useful robot systems important. For example, a robot can explore for optimal policy with trial and error using reinforcement learning. Moreover, transfer learning enables reuse of prior policy and is effective for environment adaptability. However, humans de-termine applicable methods in transfer learning. Policy selection method has been proposed for transfer learning in reinforcement learning using spreading activation model proposed in cognitive psychology. In this paper, novel activation function and spreading sequence is discussed for spreading policy selection method. Fur-ther computer simulations are used to examine the effectiveness of the proposed method for automatic policy selection in simplified shortest-path problem.

Keywords: Reinforcement learning; transfer learning; spread-ing activation theory; policy selection

Hitoshi Kono, Ren Katayama, Yusaku Takakuwa, Wen Wen and Tsuyoshi Suzuki, “Activation and Spreading Sequence for Spreading Activation Policy Selection Method in Transfer Reinforcement Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 10(12), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0101202

@article{Kono2019,
title = {Activation and Spreading Sequence for Spreading Activation Policy Selection Method in Transfer Reinforcement Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0101202},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0101202},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {12},
author = {Hitoshi Kono and Ren Katayama and Yusaku Takakuwa 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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