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math.OC2024
Meta-Learning Linear Quadratic Regulators: A Policy Gradient MAML Approach for Model-free LQR
Leonardo F. Toso, Donglin Zhan, James Anderson +1
We investigate the problem of learning linear quadratic regulators (LQR) in a multi-task, heterogeneous, and model-free setting. We characterize the stability and personalization g…
math.OC2024
Asynchronous Heterogeneous Linear Quadratic Regulator Design
Leonardo F. Toso, Han Wang, James Anderson
We address the problem of designing an LQR controller in a distributed setting, where M similar but not identical systems share their locally computed policy gradient (PG) estimate…