120 citations · 370 across the 36 of their papers we have counts for
7 papers · 1 filter
Hamilton-Jacobi Deep Q-Learning for Deterministic Continuous-Time Systems with Lipschitz Continuous Controls
Jeongho Kim, Jaeuk Shin, Insoon Yang
In this paper, we propose Q-learning algorithms for continuous-time deterministic optimal control problems with Lipschitz continuous controls. Our method is based on a new class of…
Hamilton-Jacobi-Bellman Equations for Maximum Entropy Optimal Control
Jeongho Kim, Insoon Yang
Maximum entropy reinforcement learning (RL) methods have been successfully applied to a range of challenging sequential decision-making and control tasks. However, most of existing…
Multi-Objective Predictive Taxi Dispatch via Network Flow Optimization
Beomjun Kim, Jeongho Kim, Subin Huh +2
In this paper, we discuss a large-scale fleet management problem in a multi-objective setting. We aim to seek a receding horizon taxi dispatch solution that serves as many ride req…
Minimax control of ambiguous linear stochastic systems using the Wasserstein metric
Kihyun Kim, Insoon Yang
In this paper, we propose a minimax linear-quadratic control method to address the issue of inaccurate distribution information in practical stochastic systems. To construct a cont…
Learning-based distributionally robust motion control with Gaussian processes
Astghik Hakobyan, Insoon Yang
Safety is a critical issue in learning-based robotic and autonomous systems as learned information about their environments is often unreliable and inaccurate. In this paper, we pr…
Safe reinforcement learning for probabilistic reachability and safety specifications: A Lyapunov-based approach
Subin Huh, Insoon Yang
Emerging applications in robotics and autonomous systems, such as autonomous driving and robotic surgery, often involve critical safety constraints that must be satisfied even when…
Wasserstein Distributionally Robust Motion Control for Collision Avoidance Using Conditional Value-at-Risk
Astghik Hakobyan, Insoon Yang
In this paper, a risk-aware motion control scheme is considered for mobile robots to avoid randomly moving obstacles when the true probability distribution of uncertainty is unknow…