3 citations · 6 across the 7 of their papers we have counts for
6 papers · 1 filter
Asynchronous Parallel Policy Gradient Methods for the Linear Quadratic Regulator
Xingyu Sha, Feiran Zhao, Keyou You
Learning policies in an asynchronous parallel way is essential to the numerous successes of RL for solving large-scale problems. However, their convergence performance is still not…
Policy Gradient Methods for the Cost-Constrained LQR: Strong Duality and Global Convergence
Feiran Zhao, Keyou You
In safety-critical applications, reinforcement learning (RL) needs to consider safety constraints. However, theoretical understandings of constrained RL for continuous control are…
Data-Enabled Policy Optimization for Direct Adaptive Learning of the LQR
Feiran Zhao, Florian Dörfler, Alessandro Chiuso +1
Direct data-driven design methods for the linear quadratic regulator (LQR) mainly use offline or episodic data batches, and their online adaptation has been acknowledged as an open…
Data-enabled Policy Optimization for the Linear Quadratic Regulator
Feiran Zhao, Florian Dörfler, Keyou You
Policy optimization (PO), an essential approach of reinforcement learning for a broad range of system classes, requires significantly more system data than indirect (identification…
Data-driven Control of Unknown Linear Systems via Quantized Feedback
Feiran Zhao, Xingchen Li, Keyou You
Control using quantized feedback is a fundamental approach to system synthesis with limited communication capacity. In this paper, we address the stabilization problem for unknown…
Infinite-horizon Risk-constrained Linear Quadratic Regulator with Average Cost
Feiran Zhao, Keyou You, Tamer Basar
The behaviour of a stochastic dynamical system may be largely influenced by those low-probability, yet extreme events. To address such occurrences, this paper proposes an infinite-…