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Found 5 papers, 3 papers with code
Duality-Based Stochastic Policy Optimization for Estimation with Unknown Noise Covariances
Specifically, building on the duality between synthesizing optimal control and estimation gains, the filter design problem is formalized as direct policy learning.
Data-Driven Structured Policy Iteration for Homogeneous Distributed Systems
Control of networked systems, comprised of interacting agents, is often achieved through modeling the underlying interactions.
On Controllability and Persistency of Excitation in Data-Driven Control: Extensions of Willems' Fundamental Lemma
Willems' fundamental lemma asserts that all trajectories of a linear time-invariant system can be obtained from a finite number of measured ones, assuming that controllability and a persistency of excitation condition hold.
Deep Learning-based Resource Allocation for Infrastructure Resilience
From an optimization point of view, resource allocation is one of the cornerstones of research for addressing limiting factors commonly arising in applications such as power outages and traffic jams.
On Regularizability and its Application to Online Control of Unstable LTI Systems
Learning, say through direct policy updates, often requires assumptions such as knowing a priori that the initial policy (gain) is stabilizing, or persistently exciting (PE) input-output data, is available.
