Search Results for author: Motoya Ohnishi

Found 5 papers, 2 papers with code

Koopman Spectrum Nonlinear Regulator and Provably Efficient Online Learning

1 code implementation30 Jun 2021 Motoya Ohnishi, Isao Ishikawa, Kendall Lowrey, Masahiro Ikeda, Sham Kakade, Yoshinobu Kawahara

In this work, we present a novel paradigm of controlling nonlinear systems via the minimization of the Koopman spectrum cost: a cost over the Koopman operator of the controlled dynamics.

online learning reinforcement-learning

Information Theoretic Regret Bounds for Online Nonlinear Control

1 code implementation NeurIPS 2020 Sham Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi, Wen Sun

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.

Continuous Control

Constraint Learning for Control Tasks with Limited Duration Barrier Functions

no code implementations26 Aug 2019 Motoya Ohnishi, Gennaro Notomista, Masashi Sugiyama, Magnus Egerstedt

When deploying autonomous agents in unstructured environments over sustained periods of time, adaptability and robustness oftentimes outweigh optimality as a primary consideration.

Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces

no code implementations NeurIPS 2018 Motoya Ohnishi, Masahiro Yukawa, Mikael Johansson, Masashi Sugiyama

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.

Atari Games Gaussian Processes +1

Barrier-Certified Adaptive Reinforcement Learning with Applications to Brushbot Navigation

no code implementations29 Jan 2018 Motoya Ohnishi, Li Wang, Gennaro Notomista, Magnus Egerstedt

This paper presents a safe learning framework that employs an adaptive model learning algorithm together with barrier certificates for systems with possibly nonstationary agent dynamics.

reinforcement-learning

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