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Imitation and Transfer Learning for LQG Control
In this paper we study an imitation and transfer learning setting for Linear Quadratic Gaussian (LQG) control, where (i) the system dynamics, noise statistics and cost function are unknown and expert data is provided (that is, sequences of optimal inputs and outputs) to learn the LQG controller, and (ii) multiple control tasks are performed for the same system but with different LQG costs.
Behavioral Feedback for Optimal LQG Control
In this work, we revisit the Linear Quadratic Gaussian (LQG) optimal control problem from a behavioral perspective.
Learning Lipschitz Feedback Policies from Expert Demonstrations: Closed-Loop Guarantees, Generalization and Robustness
In this work, we propose a framework to learn feedback control policies with guarantees on closed-loop generalization and adversarial robustness.
Lipschitz Bounds and Provably Robust Training by Laplacian Smoothing
In contrast to regularization-based approaches, we formulate the adversarially robust learning problem as one of loss minimization with a Lipschitz constraint, and show that the saddle point of the associated Lagrangian is characterized by a Poisson equation with weighted Laplace operator.
