30 citations · 52 across the 14 of their papers we have counts for
4 papers · 1 filter
Robust Recurrent Reinforcement Learning under Evolving Hidden Disturbances with Application to Rover Wheel Slip
Saki Omi, Hyo-Sang Shin, Namhoon Cho +2
Reinforcement learning (RL) performs well in continuous-control tasks, but evolving hidden disturbances create partial observability: the agent must infer decision-relevant latent…
Bayesian Learning Approach to Model Predictive Control
Namhoon Cho, Seokwon Lee, Hyo-Sang Shin +1
This study presents a Bayesian learning perspective towards model predictive control algorithms. High-level frameworks have been developed separately in the earlier studies on Baye…
A Learning-Based Computational Impact Time Guidance
Zichao Liu, Jiang Wang, Shaoming He +2
This paper investigates the problem of impact-time-control and proposes a learning-based computational guidance algorithm to solve this problem. The proposed guidance algorithm is…
Scalable Partial Explainability in Neural Networks via Flexible Activation Functions
Schyler C. Sun, Chen Li, Zhuangkun Wei +2
Achieving transparency in black-box deep learning algorithms is still an open challenge. High dimensional features and decisions given by deep neural networks (NN) require new algo…