5 citations · 10 across the 5 of their papers we have counts for
5 papers
Policy Gradient for Robust Markov Decision Processes
Qiuhao Wang, Shaohang Xu, Chin Pang Ho +1
We develop a generic policy gradient method with the global optimality guarantee for robust Markov Decision Processes (MDPs). While policy gradient methods are widely used for solv…
Policy Gradient in Robust MDPs with Global Convergence Guarantee
Qiuhao Wang, Chin Pang Ho, Marek Petrik
Robust Markov decision processes (RMDPs) provide a promising framework for computing reliable policies in the face of model errors. Many successful reinforcement learning algorithm…
Global Convergence of Over-parameterized Deep Equilibrium Models
Zenan Ling, Xingyu Xie, Qiuhao Wang +2
A deep equilibrium model (DEQ) is implicitly defined through an equilibrium point of an infinite-depth weight-tied model with an input-injection. Instead of infinite computations,…
Optimization Induced Equilibrium Networks
Xingyu Xie, Qiuhao Wang, Zenan Ling +4
Implicit equilibrium models, i.e., deep neural networks (DNNs) defined by implicit equations, have been becoming more and more attractive recently. In this paper, we investigate an…
Implicit Normalizing Flows
Cheng Lu, Jianfei Chen, Chongxuan Li +2
Normalizing flows define a probability distribution by an explicit invertible transformation . In this work, we present implicit…