50 citations · 79 across the 7 of their papers we have counts for
8 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…
Robust Phi-Divergence MDPs
Chin Pang Ho, Marek Petrik, Wolfram Wiesemann
In recent years, robust Markov decision processes (MDPs) have emerged as a prominent modeling framework for dynamic decision problems affected by uncertainty. In contrast to classi…
On Data-Driven Prescriptive Analytics with Side Information: A Regularized Nadaraya-Watson Approach
Prateek R. Srivastava, Yijie Wang, Grani A. Hanasusanto +1
We consider generic stochastic optimization problems in the presence of side information which enables a more insightful decision. The side information constitutes observable exoge…
Partial Policy Iteration for L1-Robust Markov Decision Processes
Chin Pang Ho, Marek Petrik, Wolfram Wiesemann
Robust Markov decision processes (MDPs) allow to compute reliable solutions for dynamic decision problems whose evolution is modeled by rewards and partially-known transition proba…
Newton-type Multilevel Optimization Method
Chin Pang Ho, Michal Kocvara, Panos Parpas
Inspired by multigrid methods for linear systems of equations, multilevel optimization methods have been proposed to solve structured optimization problems. Multilevel methods make…