101 citations · 332 across the 32 of their papers we have counts for
3 papers · 1 filter
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…
On the convex formulations of robust Markov decision processes
Julien Grand-Clément, Marek Petrik
Robust Markov decision processes (MDPs) are used for applications of dynamic optimization in uncertain environments and have been studied extensively. Many of the main properties a…