1 citations · 1 across the 4 of their papers we have counts for
4 papers
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…
Nonlinear receding-horizon differential game for drone racing along a three-dimensional path
Kijin Sung, Kenta Hoshino, Akihiko Honda +2
Drone racing requires high-speed navigation through three-dimensional paths, posing significant challenges in control engineering. Existing control methods lack a feedback control…
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…
Physics-Informed Representation and Learning: Control and Risk Quantification
Zhuoyuan Wang, Reece Keller, Xiyu Deng +3
Optimal and safety-critical control are fundamental problems for stochastic systems, and are widely considered in real-world scenarios such as robotic manipulation and autonomous d…