19 citations · 40 across the 13 of their papers we have counts for
6 papers · 1 filter
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…
A Probabilistic Approach to Satisfiability of Propositional Logic Formulae
Reazul Hasan Russel
We propose a version of WalkSAT algorithm, named as BetaWalkSAT. This method uses probabilistic reasoning for biasing the starting state of the local search algorithm. Beta distrib…
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…
Robust Exploration with Tight Bayesian Plausibility Sets
Reazul H. Russel, Tianyi Gu, Marek Petrik
Optimism about the poorly understood states and actions is the main driving force of exploration for many provably-efficient reinforcement learning algorithms. We propose optimism…
Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs
Marek Petrik, Reazul Hasan Russell
Robust MDPs (RMDPs) can be used to compute policies with provable worst-case guarantees in reinforcement learning. The quality and robustness of an RMDP solution are determined by…
A Short Survey on Probabilistic Reinforcement Learning
Reazul Hasan Russel
A reinforcement learning agent tries to maximize its cumulative payoff by interacting in an unknown environment. It is important for the agent to explore suboptimal actions as well…