101 citations · 332 across the 32 of their papers we have counts for
34 papers
Solving Multi-Model MDPs by Coordinate Ascent and Dynamic Programming
Xihong Su, Marek Petrik
Multi-model Markov decision process (MMDP) is a promising framework for computing policies that are robust to parameter uncertainty in MDPs. MMDPs aim to find a policy that maximiz…
Data Poisoning Attacks on Off-Policy Policy Evaluation Methods
Elita Lobo, Harvineet Singh, Marek Petrik +2
Off-policy Evaluation (OPE) methods are a crucial tool for evaluating policies in high-stakes domains such as healthcare, where exploration is often infeasible, unethical, or expen…
Bayesian Regret Minimization in Offline Bandits
Marek Petrik, Guy Tennenholtz, Mohammad Ghavamzadeh
We study how to make decisions that minimize Bayesian regret in offline linear bandits. Prior work suggests that one must take actions with maximum lower confidence bound (LCB) on…
On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes
Jia Lin Hau, Erick Delage, Mohammad Ghavamzadeh +1
Optimizing static risk-averse objectives in Markov decision processes is difficult because they do not admit standard dynamic programming equations common in Reinforcement Learning…
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…
RASR: Risk-Averse Soft-Robust MDPs with EVaR and Entropic Risk
Jia Lin Hau, Marek Petrik, Mohammad Ghavamzadeh +1
Prior work on safe Reinforcement Learning (RL) has studied risk-aversion to randomness in dynamics (aleatory) and to model uncertainty (epistemic) in isolation. We propose and anal…