5 citations · 7 across the 3 of their papers we have counts for
3 papers · 1 filter
Entropic Risk Constrained Soft-Robust Policy Optimization
Reazul Hasan Russel, Bahram Behzadian, Marek Petrik
Having a perfect model to compute the optimal policy is often infeasible in reinforcement learning. It is important in high-stakes domains to quantify and manage risk induced by mo…
Optimizing Norm-Bounded Weighted Ambiguity Sets for Robust MDPs
Reazul Hasan Russel, Bahram Behzadian, Marek Petrik
Optimal policies in Markov decision processes (MDPs) are very sensitive to model misspecification. This raises serious concerns about deploying them in high-stake domains. Robust M…
Optimizing Percentile Criterion Using Robust MDPs
Bahram Behzadian, Reazul Hasan Russel, Marek Petrik +1
We address the problem of computing reliable policies in reinforcement learning problems with limited data. In particular, we compute policies that achieve good returns with high c…