4 citations · 11 across the 9 of their papers we have counts for
5 papers · 1 filter
Reliably Learn to Trim Multiparametric Quadratic Programs via Constraint Removal
Zhinan Hou, Keyou You
In a wide range of applications, we are required to rapidly solve a sequence of convex multiparametric quadratic programs (mp-QPs) on resource-limited hardwares. This is a nontrivi…
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…
Learning Stabilizing Controllers of Linear Systems via Discount Policy Gradient
Feiran Zhao, Xingyun Fu, Keyou You
Stability is one of the most fundamental requirements for systems synthesis. In this paper, we address the stabilization problem for unknown linear systems via policy gradient (PG)…
Stochastic Source Seeking with Forward and Angular Velocity Regulation
Jinbiao Lin, Shiji Song, Keyou You +1
This paper studies a stochastic extremum seeking method to steer a nonholonomic vehicle to the unknown source of a static spatially distributed filed in a plane. The key challenge…