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Linear Programming Representations and Strongly Polynomial Algorithms for Robust Markov Decision Processes
We study linear programming (LP) representations and strongly polynomial algorithms for robust Markov decision processes (RMDPs) with rational polyhedral state-action rectangular uncertainty in rewards and transitions. By encoding a finite sequence of robust policy-iteration steps, we construct a si
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