24 citations · 81 across the 10 of their papers we have counts for
14 papers
Anderson Acceleration for Partially Observable Markov Decision Processes: A Maximum Entropy Approach
Mingyu Park, Jaeuk Shin, Insoon Yang
Partially observable Markov decision processes (POMDPs) is a rich mathematical framework that embraces a large class of complex sequential decision-making problems under uncertaint…
Training Wasserstein GANs without gradient penalties
Dohyun Kwon, Yeoneung Kim, Guido Montúfar +1
We propose a stable method to train Wasserstein generative adversarial networks. In order to enhance stability, we consider two objective functions using the -transform based on…
Distributionally robust risk map for learning-based motion planning and control: A semidefinite programming approach
Astghik Hakobyan, Insoon Yang
This paper proposes a novel safety specification tool, called the distributionally robust risk map (DR-risk map), for a mobile robot operating in a learning-enabled environment. Gi…
On Anderson acceleration for partially observable Markov decision processes
Melike Ermis, Mingyu Park, Insoon Yang
This paper proposes an accelerated method for approximately solving partially observable Markov decision process (POMDP) problems offline. Our method carefully combines two existin…
Distributional robustness in minimax linear quadratic control with Wasserstein distance
Kihyun Kim, Insoon Yang
To address the issue of inaccurate distributions in practical stochastic systems, a minimax linear-quadratic control method is proposed using the Wasserstein metric. Our method aim…
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…