Meta-learning operators to optimality from multi-task non-iid data
A powerful concept behind much of the recent progress in machine learning is the extraction
of common features across data from heterogeneous sources or tasks. Intuitively, using all of …
of common features across data from heterogeneous sources or tasks. Intuitively, using all of …
Sample-Efficient Linear Representation Learning from Non-IID Non-Isotropic Data
A powerful concept behind much of the recent progress in machine learning is the extraction
of common features across data from heterogeneous sources or tasks. Intuitively, using all of …
of common features across data from heterogeneous sources or tasks. Intuitively, using all of …
Learning robust data-based LQG controllers from noisy data
This paper addresses the joint state estimation and control problems for unknown linear time-
invariant systems subject to both process and measurement noise. The aim is to redesign …
invariant systems subject to both process and measurement noise. The aim is to redesign …
Meta-learning for model-reference data-driven control
One-shot direct model-reference control design techniques, like the Virtual Reference
Feedback Tuning (VRFT) approach, offer time-saving solutions for the calibration of fixed …
Feedback Tuning (VRFT) approach, offer time-saving solutions for the calibration of fixed …
On the sample complexity of the linear quadratic gaussian regulator
AAR Al Makdah, F Pasqualetti - 2023 62nd IEEE Conference …, 2023 - ieeexplore.ieee.org
In this paper we provide direct data-driven expressions for the Linear Quadratic Regulator
(LQR), the Kalman filter, and the Linear Quadratic Gaussian (LQG) controller using a finite …
(LQR), the Kalman filter, and the Linear Quadratic Gaussian (LQG) controller using a finite …
Generalization error for portable rewards in transfer imitation learning
The reward transfer paradigm in transfer imitation learning (TIL) leverages the reward
learned via inverse reinforcement learning (IRL) in the source environment to re-optimize a …
learned via inverse reinforcement learning (IRL) in the source environment to re-optimize a …
Federated TD Learning with Linear Function Approximation under Environmental Heterogeneity
We initiate the study of federated reinforcement learning under environmental heterogeneity
by considering a policy evaluation problem. Our setup involves $ N $ agents interacting with …
by considering a policy evaluation problem. Our setup involves $ N $ agents interacting with …
A Statistical Guarantee for Representation Transfer in Multitask Imitation Learning
Transferring representation for multitask imitation learning has the potential to provide
improved sample efficiency on learning new tasks, when compared to learning from scratch …
improved sample efficiency on learning new tasks, when compared to learning from scratch …
Regret Analysis of Multi-task Representation Learning for Linear-Quadratic Adaptive Control
Representation learning is a powerful tool that enables learning over large multitudes of
agents or domains by enforcing that all agents operate on a shared set of learned features …
agents or domains by enforcing that all agents operate on a shared set of learned features …
Evaluating the stealth of reinforcement learning-based cyber attacks against unknown scenarios using knowledge transfer techniques
AJ Horta Neto, AFP dos Santos… - Journal of Computer …, 2024 - content.iospress.com
Organizations are vulnerable to cyber attacks as they rely on computer networks and the
internet for communication and data storage. While Reinforcement Learning (RL) is a widely …
internet for communication and data storage. While Reinforcement Learning (RL) is a widely …