Hugging Face Trending Papers
Quantitative Analysis of $ω$-Regular Robust MDPs
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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.
Hugging Face Trending Papers
Aug 3
Optimizing Minimax Regret in Uncertain MDPs with Small Sets of Policies
Sequential decision-making in real-world applications often involves uncertainty about the environment's model. Uncertain Markov decision processes (UMDPs) represent the possible environments as a set of MDPs with shared states and actions but potentially different transition probabilities and rewards.
arXiv Machine Learning
Aug 4
Optimizing Minimax Regret in Uncertain MDPs with Small Sets of Policies
arXiv:2608. 02509v1 Announce Type: cross Abstract: Sequential decision-making in real-world applications often involves uncertainty about the environment's model.
arXiv AI
Jul 14
Efficient Q-Learning and Actor-Critic Methods for Robust Average-Reward Reinforcement Learning
arXiv:2506. 07040v4 Announce Type: replace-cross Abstract: We study model-free methods for distributionally robust infinite-horizon average-reward Markov decision processes (MDPs).
Hugging Face Trending Papers
Sep 3
Robust PAC Learning of Concurrent Stochastic Games
We introduce the first Probably Approximately Correct (PAC) learning framework for general-sum concurrent stochastic games (CSGs) with transition uncertainty, while addressing the challenge of Nash equilibrium (NE) existence. Our algorithm maintains data-driven $L^1$ confidence sets over transition kernels and solves a robust CSG to compute a social-welfare optimal $\varepsilon$-NE, using a robust MDP-based exploration mechanism to drive joint state-action coverage.
arXiv Machine Learning
Aug 4
Near-Optimal Reinforcement Learning for Constrained Recurrence Objectives
arXiv:2511. 19849v2 Announce Type: replace-cross Abstract: Recurrence objectives, where a target region must be visited infinitely often, are a fundamental class of specifications for Markov decision processes (MDPs) and form the core of $\omega$-regular and linear temporal logic (LTL) objectives.