4 papers
Efficient Algorithms for Robust Markov Decision Processes with -Rectangular Ambiguity Sets
Chin Pang Ho, Marek Petrik, Wolfram Wiesemann
Robust Markov decision processes (MDPs) have attracted significant interest due to their ability to protect MDPs from poor out-of-sample performance in the presence of ambiguity. I…
Convergence of Fast Policy Iteration in Markov Games and Robust MDPs
Keith Badger, Jefferson Huang, Marek Petrik
Markov games and robust MDPs are closely related models that involve computing a pair of saddle point policies. As part of the long-standing effort to develop efficient algorithms…
Probabilistic Safety Guarantee for Stochastic Control Systems Using Average Reward MDPs
Saber Omidi, Marek Petrik, Se Young Yoon +1
Safety in stochastic control systems, which are subject to random noise with a known probability distribution, aims to compute policies that satisfy predefined operational constrai…
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…