arXiv AI
23h ago
Adaptive Policy Portfolios for Robust Markov Decision Processes
arXiv:2608. 17929v1 Announce Type: new Abstract: Robust Markov decision processes optimize one policy against a set of plausible transition functions.
Adaptive Policy Portfolios for Robust Markov Decision Processes
Robust Markov decision processes optimize one policy against a set of plausible transition functions. This can be conservative when the unknown dynamics are fixed and become partially identifiable after deployment.
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Evaluating AI Investment Strategies
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Strongly Polynomial Time Complexity of Policy Iteration for $L_\infty$ Robust MDPs
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Optimizing Minimax Regret in Uncertain MDPs with Small Sets of Policies
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arXiv Machine Learning
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Learning in Markovian bandits with non-observable states and constrained decision epochs
arXiv:2606. 27448v1 Announce Type: new Abstract: This paper studies the problem of regret minimization in Markovian bandits with \emph{non-observable states} and possibly \emph{constrained} decision epochs.
arXiv Machine Learning
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Conformal bandits: bringing statistical validity and reward efficiency under weak arm separability
arXiv:2512. 09850v2 Announce Type: replace Abstract: We introduce Conformal Bandits, a novel framework integrating Conformal Prediction (CP) into bandit problems, a classic paradigm for sequential decision-making under uncertainty.
arXiv Machine Learning
Jun 15
Lyapunov-Based Sample Complexity Analysis for Weakly-Coupled MDPs
arXiv:2606. 14095v1 Announce Type: new Abstract: We study the sample complexity of learning in average-reward weakly-coupled Markov decision processes (WCMDPs) and Restless Bandits (RBs) under a generative model.
arXiv Machine Learning
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Minimax-Optimal Policy Regret in Partially Observable Markov Games
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Policy Optimization Achieves Data-Dependent Regret Bounds in MDPs with Unknown Transitions
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arXiv Machine Learning
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Convex Optimization with Nested Evolving Feasible Sets
arXiv:2605. 07386v2 Announce Type: replace Abstract: \emph{Convex Optimization with Nested Evolving Feasible Sets (CONES)} is considered where the objective function \(f\) remains fixed but the feasible region evolves over time as a nested sequence \(S_1 \supseteq S_2 \supseteq \cdots \supseteq S_T\).