Derives an ε-local minimax excess-cost lower bound for learning LQG controllers from offline trajectories of a linear exploration policy, expressed via the Hessian of the LQG cost and inverse Fisher information, and instantiates it on fragile robust-control examples.
On the sample com- plexity of the linear quadratic regulator
3 Pith papers cite this work. Polarity classification is still indexing.
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Relearn LQR combines recursive least squares with policy gradient for on-policy data-driven LQR and proves stability of the full scheme via Lyapunov analysis with averaging and timescale separation.
A unified data-driven co-design method for event-triggered and sparse control in noisy NCS with unknown dynamics, providing stability and H_infty conditions via iterative optimization.
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The Fragility of Learning LQG Controllers
Derives an ε-local minimax excess-cost lower bound for learning LQG controllers from offline trajectories of a linear exploration policy, expressed via the Hessian of the LQG cost and inverse Fisher information, and instantiates it on fragile robust-control examples.
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Stability-Certified On-Policy Data-Driven LQR via Recursive Learning and Policy Gradient
Relearn LQR combines recursive least squares with policy gradient for on-policy data-driven LQR and proves stability of the full scheme via Lyapunov analysis with averaging and timescale separation.
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Data-Driven Co-Design of Event-Triggered and Sparse Control for Resource-Aware Networked Control Systems
A unified data-driven co-design method for event-triggered and sparse control in noisy NCS with unknown dynamics, providing stability and H_infty conditions via iterative optimization.