Norio Kosaka (小坂 紀夫)
email: kosakaboat[at]gmail.com

CV | Scholar | Github | LinkedIn

Norio completed an MSc in Mathematics at Birkbeck, University of London, with a thesis titled "Overview of Riemann surfaces." with the supervision of Prof. Ben Fairbairn. He also did an MSc in Machine Learning with Distinction at Royal Holloway University of London, focusing on the intersection of Reinforcement Learning and Robotics for his Master's Thesis, under the guidance of Prof. Chris Watkins.

Research interests: Mathematics (Riemann surface, Hyperbolic geometry, and Algebric geometry) and Machine / Reinforcement Learning

  Education
  Work Experience
  Mathematics
  Machine Learning
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[NEW] Enhancing Actor-Critic Decision-Making with Afterstate Models for Continuous Control
Norio Kosaka
ICML Workshop: Aligning Reinforcement Learning Experimentalists and Theorists, 2024

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Direct Preference-based Policy Optimization without Reward Modeling
Gaon An*, Junhyeok Lee*, Xingdong Zuo, Norio Kosaka, Kyung-Min Kim, Hyun Oh Song
Neural Information Processing Systems (NeurIPS), 2023

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Know Your Action Set: Learning Action Relations for Reinforcement Learning
Ayush Jain*, Norio Kosaka*, Kyung-Min Kim, Joseph J Lim
International Conference on Learning Representations (ICLR) 2022, Apr. 2022

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PlaNet of the Bayesians: Reconsidering and Improving Deep Planning Network by Incorporating Bayesian Inference
Masashi Okada, Norio Kosaka, Tadahiro Taniguchi
International Conference on Intelligent Robots and Systems(IROS) 2020, USA, Oct. 2020

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Has it explored enough
Norio Kosaka
Master's Thesis at Royal Holloway, University of London, Sep. 2019

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Hindsight Experience Replay on ROS
Norio Kosaka
ROSDevCon19, Jun. 2019

  Extracurricular Activities
  • University Official Rowing Club
    Waseda University Tokyo, Japan
    • Achieved 5th place in Men’s 8+ at the 34th All-Japan Lightweight Championship (Japan Cup) (Watch Video) (Results Table)
    • 1st place in the 2013 Waseda-Keio Regatta (Watch Video); 2nd place in the 2014 Waseda-Keio Regatta
  Access Map

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