83 citations · 132 across the 8 of their papers we have counts for
8 papers
Signatures Meet Dynamic Programming: Generalizing Bellman Equations for Trajectory Following
Motoya Ohnishi, Iretiayo Akinola, Jie Xu +2
Path signatures have been proposed as a powerful representation of paths that efficiently captures the path's analytic and geometric characteristics, having useful algebraic proper…
Dynamic Structure Estimation from Bandit Feedback using Nonvanishing Exponential Sums
Motoya Ohnishi, Isao Ishikawa, Yuko Kuroki +1
This work tackles the dynamic structure estimation problems for periodically behaved discrete dynamical system in the Euclidean space. We assume the observations become sequentiall…
Koopman Spectrum Nonlinear Regulators and Efficient Online Learning
Motoya Ohnishi, Isao Ishikawa, Kendall Lowrey +3
Most modern reinforcement learning algorithms optimize a cumulative single-step cost along a trajectory. The optimized motions are often 'unnatural', representing, for example, beh…
Information Theoretic Regret Bounds for Online Nonlinear Control
Sham Kakade, Akshay Krishnamurthy, Kendall Lowrey +2
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 Repro…
Constraint Learning for Control Tasks with Limited Duration Barrier Functions
Motoya Ohnishi, Gennaro Notomista, Masashi Sugiyama +1
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
Motoya Ohnishi, Masahiro Yukawa, Mikael Johansson +1
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 continuou…
Barrier-Certified Adaptive Reinforcement Learning with Applications to Brushbot Navigation
Motoya Ohnishi, Li Wang, Gennaro Notomista +1
This paper presents a safe learning framework that employs an adaptive model learning algorithm together with barrier certificates for systems with possibly nonstationary agent dyn…
Online Nonlinear Estimation via Iterative L2-Space Projections: Reproducing Kernel of Subspace
Motoya Ohnishi, Masahiro Yukawa
We propose a novel online learning paradigm for nonlinear-function estimation tasks based on the iterative projections in the L2 space with probability measure reflecting the stoch…