172 citations · 924 across the 40 of their papers we have counts for
8 papers · 1 filter
Unmatched uncertainty mitigation through neural network supported model predictive control
Mateus V. Gasparino, Prabhat K. Mishra, Girish Chowdhary
This paper presents a deep learning based model predictive control (MPC) algorithm for systems with unmatched and bounded state-action dependent uncertainties of unknown structure.…
Dynamic Graph Node Classification via Time Augmentation
Jiarui Sun, Mengting Gu, Chin-Chia Michael Yeh +3
Node classification for graph-structured data aims to classify nodes whose labels are unknown. While studies on static graphs are prevalent, few studies have focused on dynamic gra…
Forced Variational Integrator Networks for Prediction and Control of Mechanical Systems
Aaron Havens, Girish Chowdhary
As deep learning becomes more prevalent for prediction and control of real physical systems, it is important that these overparameterized models are consistent with physically plau…
Adaptive Policy Transfer in Reinforcement Learning
Girish Joshi, Girish Chowdhary
Efficient and robust policy transfer remains a key challenge for reinforcement learning to become viable for real-wold robotics. Policy transfer through warm initialization, imitat…
Deep Model Reference Adaptive Control
Girish Joshi, Girish Chowdhary
We present a new neuroadaptive architecture: Deep Neural Network based Model Reference Adaptive Control (DMRAC). Our architecture utilizes the power of deep neural network represen…
Learning to Cope with Adversarial Attacks
Xian Yeow Lee, Aaron Havens, Girish Chowdhary +1
The security of Deep Reinforcement Learning (Deep RL) algorithms deployed in real life applications are of a primary concern. In particular, the robustness of RL agents in cyber-ph…
Robust Deep Reinforcement Learning with Adversarial Attacks
Anay Pattanaik, Zhenyi Tang, Shuijing Liu +2
This paper proposes adversarial attacks for Reinforcement Learning (RL) and then improves the robustness of Deep Reinforcement Learning algorithms (DRL) to parameter uncertainties…
Hierarchic Kernel Recursive Least-Squares
Hossein Mohamadipanah, Mahdi Heydari, Girish Chowdhary
We present a new kernel-based algorithm for modeling evenly distributed multidimensional datasets that does not rely on input space sparsification. The presented method reorganizes…