Motoya Ohnishi — accepted AI-conference papers

2020

Information Theoretic Regret Bounds for Online Nonlinear Control

Sham Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi, Wen Sun

NeurIPS 2020poster

This work studies the problem of sequential control in an unknown, nonlinear dynamical system, where we model the underlying system dynamics as an unknown function in a known Reproducing Kernel Hilbert Space. This framework yields a general setting that permits discrete and continuous control input…

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2018

Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces

Motoya Ohnishi, Masahiro Yukawa, Mikael Johansson, Masashi Sugiyama

NeurIPS 2018poster

Motivated by the success of reinforcement learning (RL) for discrete-time tasks such as AlphaGo and Atari games, there has been a recent surge of interest in using RL for continuous-time control of physical systems (cf. many challenging tasks in OpenAI Gym and DeepMind Control Suite). Since discreti…

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