activity
20192026
most citedBenchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control with Action Constraints

24 citations · 27 across the 10 of their papers we have counts for

collaborators

10 papers

eess.SY2026

Linear Stochastic Systems with i.i.d. uncertainties: Exact Covariance Characterization, Stability Analysis and State-feedback Design

Kaouther Moussa, Dimitri Peaucelle, Yohei Hosoe +1

This paper studies linear discrete-time systems affected by independent and identically distributed (i.i.d.) multiplicative uncertainties and additive noise. It establishes the mai…

cs.LG2025

Provably Efficient RL under Episode-Wise Safety in Constrained MDPs with Linear Function Approximation

Toshinori Kitamura, Arnob Ghosh, Tadashi Kozuno +5

We study the reinforcement learning (RL) problem in a constrained Markov decision process (CMDP), where an agent explores the environment to maximize the expected cumulative reward…

cs.LG2024

Near-Optimal Policy Identification in Robust Constrained Markov Decision Processes via Epigraph Form

Toshinori Kitamura, Tadashi Kozuno, Wataru Kumagai +6

Designing a safe policy for uncertain environments is crucial in real-world control systems. However, this challenge remains inadequately addressed within the Markov decision proce…

cs.LG2023★ 24 cited

Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control with Action Constraints

Kazumi Kasaura, Shuwa Miura, Tadashi Kozuno +3

This study presents a benchmark for evaluating action-constrained reinforcement learning (RL) algorithms. In action-constrained RL, each action taken by the learning system must co…

eess.SY2022

H2 Performance Analysis and Synthesis for Discrete-Time Linear Systems with Dynamics Determined by an i.i.d. Process

Yohei Hosoe, Takashi Okamoto, Tomomichi Hagiwara

This paper is concerned with H2 control of discrete-time linear systems with dynamics determined by an independent and identically distributed (i.i.d.) process. A definition of H2…

eess.SY2022

Stochastic Aperiodic Control of Networked Systems with i.i.d. Time-Varying Communication Delays

Yohei Hosoe

This paper studies stochastic aperiodic stabilization of a networked control system (NCS) consisting of a continuous-time plant and a discrete-time controller. The plant and the co…

eess.SY2021★ 2 cited

Contraction Analysis of Discrete-time Stochastic Systems

Yu Kawano, Yohei Hosoe

In this paper, we develop a novel contraction framework for stability analysis of discrete-time nonlinear systems with parameters following stochastic processes. For general stocha…

eess.SY2019★ 1 cited

On Second-Moment Stability of Discrete-Time Linear Systems with General Stochastic Dynamics

Yohei Hosoe, Tomomichi Hagiwara

This paper provides a new unified framework for second-moment stability of discrete-time linear systems with stochastic dynamics. Relations of notions of second-moment stability ar…

eess.SY2019

Distribution Modeling and Stabilization Control for Discrete-Time Linear Random Dynamical Systems Using Ensemble Kalman Filter

Yohei Hosoe, Dimitri Peaucelle

This paper studies an output feedback stabilization control framework for discrete-time linear systems with stochastic dynamics determined by an independent and identically distrib…

eess.SY2019

Equivalent Stability Notions, Lyapunov Inequality, and Its Application in Discrete-Time Linear Systems with Stochastic Dynamics Determined by an i.i.d. Process

Yohei Hosoe, Tomomichi Hagiwara

This paper is concerned with stability analysis and synthesis for discrete-time linear systems with stochastic dynamics. Equivalence is first proved for three stability notions und…