15 citations · 26 across the 5 of their papers we have counts for
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math.OC2023
Learning the Kalman Filter with Fine-Grained Sample Complexity
Xiangyuan Zhang, Bin Hu, Tamer Başar
We develop the first end-to-end sample complexity of model-free policy gradient (PG) methods in discrete-time infinite-horizon Kalman filtering. Specifically, we introduce the rece…
math.OC2023
Revisiting LQR Control from the Perspective of Receding-Horizon Policy Gradient
Xiangyuan Zhang, Tamer Başar
We revisit in this paper the discrete-time linear quadratic regulator (LQR) problem from the perspective of receding-horizon policy gradient (RHPG), a newly developed model-free le…
math.OC2021★ 8 cited
Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity
Kaiqing Zhang, Xiangyuan Zhang, Bin Hu +1
Direct policy search serves as one of the workhorses in modern reinforcement learning (RL), and its applications in continuous control tasks have recently attracted increasing atte…