Updated on 2025/09/12

 
IMAGAWA Takahisa
 
Scopus Paper Info  
Total Paper Count: 0  Total Citation Count: 0  h-index: 4

Citation count denotes the number of citations in papers published for a particular year.

Affiliation
Faculty of Computer Science and Systems Engineering Department of Intelligent and Control Systems
Job
Assistant Professor
External link

Research Areas

  • Informatics / Intelligent informatics  / Artificial Intelligence, Reinforcement Learning, Planning

Degree

  • 東京大学  -  博士（学術）   2018.03

  • 東京大学  -  修士（学術）   2015.03

  • 東京大学  -  学士（教養）   2013.03

Biography in Kyutech

  • 2024.02
     

    Kyushu Institute of Technology   Faculty of Computer Science and Systems Engineering   Department of Intelligent and Control Systems   Assistant Professor  

Papers

  • ハンドクラフト特徴量による深層学習ベースの三次元物体検出器の性能向上

    平川絢士, 今川孝久, 榎田修一

    DIA2025講演論文集   2025.03

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    Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)

    Kyutacar

  • 事前知識を用いたQ学習

    今川孝久，榎田修一

    IBISML研究会論文集   2024.12

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    Authorship：Lead author,　Corresponding author   Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)

    Kyutacar

  • A Method for Efficiently Selecting the Number of Dimension in Deep LearningFocusing on Change in Loss in the Early Phase of Learning "jointly worked"

    Uchida, Aoto/ Imagawa, Takahisa/ Enokida, Shuichi

    2024.09

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    Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)

    Kyutacar

  • 人物の重なりに対する頑健性向上のためのマスク処理を用いた姿勢推定

    井手宥希，今川孝久，榎田修一

    MIRU2024 Extended Abstract集   2024.08

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    Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)

    Kyutacar

  • Diffusion Action Segmentationにおけるフレーム特徴量へのマスキングに関する研究

    大山佑太，今川孝久，榎田修一

    MIRU2024 Extended Abstract集   2024.08

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    Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)

    Kyutacar

  • DROPOUT Q-FUNCTIONS FOR DOUBLY EFFICIENT REINFORCEMENT LEARNING Reviewed International journal

    Hiraoka T., Imagawa T., Hashimoto T., Onishi T., Tsuruoka Y.

    ICLR 2022 - 10th International Conference on Learning Representations   2022.01

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    Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)

    Scopus

    Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85146871335&origin=inward

  • Off-Policy Meta-Reinforcement Learning with Belief-Based Task Inference Reviewed International journal

    Imagawa T., Hiraoka T., Tsuruoka Y.

    IEEE Access   10   49494 - 49507   2022.01

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    Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)

    DOI： 10.1109/ACCESS.2022.3170582

    Scopus

    Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85129665822&origin=inward

  • Meta-Model-Based Meta-Policy Optimization Reviewed International journal

    Hiraoka T., Imagawa T., Tangkaratt V., Osa T., Onishi T., Tsuruoka Y.

    Proceedings of Machine Learning Research   157   129 - 144   2021.01

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    Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

    Scopus

    Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85137101456&origin=inward

  • Learning robust options by conditional value at risk optimization Reviewed International journal

    Hiraoka T., Imagawa T., Mori T., Onishi T., Tsuruoka Y.

    Advances in Neural Information Processing Systems   32   2019.01

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    Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

    Scopus

    Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85090171999&origin=inward

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Conference Prsentations (Oral, Poster)

  • Q-Learning with Prior Knowledge

    Takahisa Imagawa

    2024.12 

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    Event date： 2025.12.20 - 2025.12.21   Language：Japanese   Country：Japan  

Grants-in-Aid for Scientific Research

  • 他者データを学習のバイアスとして活用することによる強化学習の効率化

    Grant number：25K21153   2025.04   若手研究

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    強化学習は学習者が試行錯誤を繰り返しその結果の良し悪しをもとに学習する方法で，広い応用先を持つ．
    しかし，多くの場合，無数の試行錯誤が必要となり，このことが実応用を妨げる一因となっている．
    本研究では，強化学習をする学習者の他に，他者が存在する状況を想定する．
    その場合，他者が学習者にとって有益な行動をとれば，学習者の学習が大きく進展する可能性がある．
    本研究では他者からの学習を実現するための基礎となる手法を考案する．