arXiv AI
Jun 3
Strongly Polynomial Time Complexity of Policy Iteration for $L_\infty$ Robust MDPs
arXiv:2601. 23229v2 Announce Type: replace Abstract: Markov decision processes (MDPs) are a fundamental model in sequential decision making.
Linear Programming Representations and Strongly Polynomial Algorithms for Robust Markov Decision Processes
The paper presents linear programming formulations 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, a single LP is constructed whose optimal solutions recover the robust optimal value and all optimal stationary randomized policies. The authors provide a general complexity analysis of robust policy iteration, improving known bounds for α1 and α1∞ RMDPs and establishing new strongly polynomial bounds for general interval, weighted α1, and Wasserstein RMDPs, as well as turn‑based stochastic games with these uncertainty sets.
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arXiv AI
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arXiv AI
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Hugging Face Trending Papers
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Adaptive Policy Portfolios for Robust Markov Decision Processes
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arXiv Machine Learning
2d ago
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arXiv Machine Learning
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arXiv AI
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arXiv Machine Learning
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arXiv Machine Learning
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Hugging Face Trending Papers
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Optimizing Minimax Regret in Uncertain MDPs with Small Sets of Policies
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