10 citations · 22 across the 7 of their papers we have counts for
7 papers
Reinforcement Learning for Control with Probabilistic Stability Guarantee: A Finite-Sample Approach
Minghao Han, Lixian Zhang, Chenliang Liu +3
This paper presents a novel approach to reinforcement learning (RL) for control systems that provides probabilistic stability guarantees using finite data. Leveraging Lyapunov's me…
Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functions
Desong Du, Shaohang Han, Naiming Qi +3
Reinforcement learning (RL) exhibits impressive performance when managing complicated control tasks for robots. However, its wide application to physical robots is limited by the a…
An Analytical Formula for Stability Sensitivity Using SDP Dual
Jun Wang, Yue Song, David John Hill +1
In this letter, we analytically investigate the sensitivity of stability index to its dependent variables in general power systems. Firstly, we give a small-signal model, the stabi…
Effects of Safety State Augmentation on Safe Exploration
Aivar Sootla, Alexander I. Cowen-Rivers, Jun Wang +1
Safe exploration is a challenging and important problem in model-free reinforcement learning (RL). Often the safety cost is sparse and unknown, which unavoidably leads to constrain…
Reinforcement Learning Control of Constrained Dynamic Systems with Uniformly Ultimate Boundedness Stability Guarantee
Minghao Han, Yuan Tian, Lixian Zhang +2
Reinforcement learning (RL) is promising for complicated stochastic nonlinear control problems. Without using a mathematical model, an optimal controller can be learned from data e…
Actor-Critic Reinforcement Learning for Control with Stability Guarantee
Minghao Han, Lixian Zhang, Jun Wang +1
Reinforcement Learning (RL) and its integration with deep learning have achieved impressive performance in various robotic control tasks, ranging from motion planning and navigatio…