Search Results for author:
Found 7 papers, 2 papers with code
Information Theoretic Regret Bounds for Online Nonlinear Control
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.
Koopman Spectrum Nonlinear Regulator and Provably Efficient Online Learning
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.
Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces
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.
Barrier-Certified Adaptive Reinforcement Learning with Applications to Brushbot Navigation
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.
Constraint Learning for Control Tasks with Limited Duration Barrier Functions
When deploying autonomous agents in unstructured environments over sustained periods of time, adaptability and robustness oftentimes outweigh optimality as a primary consideration.
Dynamic Structure Estimation from Bandit Feedback
This work present novel method for structure estimation of an underlying dynamical system.
Signatures Meet Dynamic Programming: Generalizing Bellman Equations for Trajectory Following
As a specific case of our framework, we devise a model predictive control method for path tracking.
