In this work, we study policy optimization under domain randomization for linear quadratic control, focusing on learning a single state-feedback controller that minimizes the average cost across systems with uncertain dynamics. We propose a policy iteration algorithm with a step-size rule that preserves stability acros...
This work first study PG of domain randomized LQR over history-dependent policy classes, such as finite impulse response controllers, as they can extend the possibilities of simultaneous stabilization, and proposes a curriculum learning based algorithm which gradually expands the memory of the controller.
A verification framework to numerically analyze inexact model predictive controllers (MPCs) in the constrained non-linear discrete-time setting to formulate an optimization problem that searches over the worst-case initial state within a given set and control inputs consistent with the inexact controller to maximize a...
Generalized semi-infinite programs (GSIPs) arise in robust optimal control whenever the admissible uncertainty depends on the state or controls. Existing GSIP methods either impose restrictive structural assumptions or require global optimization that scales poorly to control problems. We present a general framework th...
This letter develops a time-parallel policy-gradient framework for discrete-time nonlinear control-affine systems and proves that, for any finite horizon T, the state solver recovers the exact trajectory from any initialization in at most T iterations.
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