A scalar-projection federated zeroth-order method for model-free LQR policy learning that reduces per-agent communication from O(d) to O(1) with convergence rate improving in the number of agents.
Model-free Learning with Heterogeneous Dynamical Systems: A Federated LQR Approach
2 Pith papers cite this work. Polarity classification is still indexing.
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Multitask LQG control via history-dependent lifting to LQR yields generalization bounds tied to bisimulation heterogeneity and reduces policy gradient variance proportionally to the number of training tasks.
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Scalar Federated Learning for Linear Quadratic Regulator
A scalar-projection federated zeroth-order method for model-free LQR policy learning that reduces per-agent communication from O(d) to O(1) with convergence rate improving in the number of agents.
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Multitask LQG Control: Performance and Generalization Bounds
Multitask LQG control via history-dependent lifting to LQR yields generalization bounds tied to bisimulation heterogeneity and reduces policy gradient variance proportionally to the number of training tasks.