Linear Programming Representations and Strongly Polynomial Algorithms for Robust Markov Decision Processes
The paper claims a single LP can recover robust optimal values and all optimal stationary randomized policies for a broad class of RMDPs.
Zhong and Ye handle robust Markov decision processes with rational polyhedral, state-action rectangular uncertainty in rewards and transitions. The construction encodes a finite run of robust policy iteration into one linear program, with polynomial size and strongly polynomial construction time when the discount factor is fixed. They also give complexity bounds for several uncertainty sets, including `l1`, `l∞`, interval, weighted `l1`, and Wasserstein cases, plus related turn-based stochastic games. Source: ArXiv · AI/CL/LG's note.
Zhong and Ye handle robust Markov decision processes with rational polyhedral, state-action rectangular uncertainty in rewards and transitions. The construction encodes a finite run of robust policy iteration into one linear program, with polynomial size and strongly polynomial construction time when the discount factor is fixed. They also give complexity bounds for several uncertainty sets, including `l1`, `l∞`, interval, weighted `l1`, and Wasserstein cases, plus related turn-based stochastic games. Source: ArXiv · AI/CL/LG's note.
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