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Found 7 papers, 1 papers with code
Towards Model-Free LQR Control over Rate-Limited Channels
Toward answering this question, we study a setting where a worker agent transmits quantized policy gradients (of the LQR cost) to a server over a noiseless channel with a finite bit-rate.
Learning Dynamical Systems by Leveraging Data from Similar Systems
We consider the problem of learning the dynamics of a linear system when one has access to data generated by an auxiliary system that shares similar (but not identical) dynamics, in addition to data from the true system.
Learning Decentralized Linear Quadratic Regulator with $\sqrt{T}$ Regret
Under the assumption that the system is stable or given a known stabilizing controller, we show that our controller enjoys an expected regret that scales as $\sqrt{T}$ with the time horizon $T$ for the case of partially nested information pattern.
Identifying the Dynamics of a System by Leveraging Data from Similar Systems
We study the problem of identifying the dynamics of a linear system when one has access to samples generated by a similar (but not identical) system, in addition to data from the true system.
On the Sample Complexity of Decentralized Linear Quadratic Regulator with Partially Nested Information Structure
We study the problem of control policy design for decentralized state-feedback linear quadratic control with a partially nested information structure, when the system model is unknown.
Parameter Estimation in Epidemic Spread Networks Using Limited Measurements
We study the problem of estimating the parameters (i. e., infection rate and recovery rate) governing the spread of epidemics in networks.
Near-Optimal Data Source Selection for Bayesian Learning
We then show that the data source selection problem can be transformed into an instance of the submodular set covering problem studied in the literature, and provide a standard greedy algorithm to solve the data source selection problem with provable performance guarantees.
