activity
20232025
most citedRates of Convergence in Certain Native Spaces of Approximations used in Reinforcement Learning

2 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

eess.SY2025

Functional Uncertainty Classes, Nonparametric Adaptive Contro Functional Uncertainty Classes for Nonparametric Adaptive Control: the Curse of Dimensionality

Haoran Wang, Shengyuan Niu, Henry Moon +4

This paper derives a new class of vector-valued reproducing kernel Hilbert spaces (vRKHS) defined in terms of operator-valued kernels for the representation of functional uncertain…

math.OC2025

Tailoring Reproducing Kernels for Optimal Control via Policy Iteration

Shengyuan Niu, Ali Bouland, Haoran Wang +5

This paper presents a novel approach to formulating the actor-critic method for optimal control by casting policy iteration in reproducing kernel Hilbert spaces (RKHSs -- also know…

eess.SY2025

Vector-Valued Native Space Embedding for Adaptive State Observation

Shengyuan Niu, Haoran Wang, Heejip Moon +3

This paper combines vector-valued reproducing kernel Hilbert space (vRKHS) embedding with robust adaptive observation, yielding an algorithm that is both non-parametric and robust.…

math.OC2024

Convergence Rates of Online Critic Value Function Approximation in Native Spaces

Shengyuan Niu, Ali Bouland, Haoran Wang +5

In this paper, the evolution equation that defines the online critic for the approximation of the optimal value function is cast in a general class of reproducing kernel Hilbert sp…

eess.SY20232 cited

Rates of Convergence in Certain Native Spaces of Approximations used in Reinforcement Learning

Ali Bouland, Shengyuan Niu, Sai Tej Paruchuri +3

This paper studies convergence rates for some value function approximations that arise in a collection of reproducing kernel Hilbert spaces (RKHS) . By casting an optimal con…