arXiv Machine Learning

Robust PAC Learning of Concurrent Stochastic Games

The paper presents the first PAC learning framework for general-sum concurrent stochastic games with uncertain transitions, addressing the challenge of Nash equilibrium existence. It introduces data‑driven L¹ confidence sets over transition kernels and a robust CSG solver that computes a social‑welfare optimal ε‑NE, or provides a certificate that no exact NE exists. The algorithm achieves polynomial sample complexity under a minimum reachability condition and is validated on benchmark CSGs with near‑optimal performance.

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.

benchmarks

arXiv Machine Learning

Oct 3

Fully Online Decentralized Learning in Stochastic Games with Unknown Independent Chains

arXiv:2610. 01181v1 Announce Type: new Abstract: We consider stochastic games with independent controlled chains and unknown transition kernels, where players observe only their local states and realized payoffs.

By S. Rasoul Etesami

arXiv Machine Learning

Jun 26

Decentralized Best-Response-Based Learning in Two-Player Zero-Sum Stochastic Games: A Finite-Sample Analysis

arXiv:2409. 01447v3 Announce Type: replace Abstract: We present a finite-sample analysis of decentralized learning in two-player zero-sum matrix games and stochastic games, with a focus on best-response-based learning algorithms.

By Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar, Asuman Ozdaglar, Adam Wierman

arXiv AI

Oct 15

Certifying Almost-Sure Reachability in Unknown MDPs

arXiv:2610.18119v1 Announce Type: cross Abstract: {\em Specification-Guided Reinforcement Learning (RL)} has largely concerned itself with \emph{learning} an optimal policy against a formal specifica...

By S Akshay, Suguman Bansal, Gaurav Mahla

reinforcement-learning safety

arXiv Machine Learning

Jul 17

PAC Learning in Turn-Based Stochastic Games with Reachability Objectives: A Decentralized Private Approach via Expected Conditional Distance

arXiv:2607. 14877v1 Announce Type: new Abstract: Reachability is the most fundamental logical objective, yet it is notoriously difficult to learn in reinforcement learning settings: even for Markov decision processes, PAC learning of reachability is impossible without additional assumptions.

By Ali Asadi, Krishnendu Chatterjee, Pavol Kebis

reinforcement-learning safety

arXiv AI

6d ago

Provably Efficient Q-learning for Robust Reinforcement Learning

The paper introduces a sample‑efficient asynchronous Q‑learning algorithm for robust reinforcement learning in infinite‑horizon, discounted robust MDPs with total‑variation uncertainty. It proves that, given a single Markovian trajectory, the algorithm achieves an entrywise ε‑approximation of the optimal Q‑function with a sample complexity bounded by a nearly minimax‑optimal expression involving the discount factor, uncertainty level, mixing time, and occupancy probability. This result improves significantly over prior bounds for robust RL.

By Yuchen Jiao, Gen Li

agents reinforcement-learning robotics

arXiv AI

Oct 15

Scalable Robust MCTS with Anytime Deterministic Optimality Guarantees

arXiv:2610.15461v1 Announce Type: new Abstract: MDP planners often rely on nominal transition models that may differ from the true dynamics. Robust MDP planning accounts for this uncertainty through...

By Tomer Massas, Vadim Indelman

efficiency

Hugging Face Trending Papers

Aug 26

Quantitative Analysis of $ω$-Regular Robust MDPs

Robust Markov Decision Processes (RMDPs) generalize classical MDPs by allowing uncertainty in transition probabilities and optimizing against their worst-case realization. We consider $(s,a)$-rectangu...

agents reinforcement-learning safety

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.

By Tianhao Wu, Matthew Zurek, Weina Wang, Qiaomin Xie

reinforcement-learning safety

arXiv Machine Learning

Oct 7

Linear Bandits under Exact Sliding-Window Constraints

arXiv:2610. 08745v1 Announce Type: new Abstract: We study linear bandits under exact sliding-window constraints, where every consecutive block of actions must belong to a prescribed feasible set.

By Seyed Mohammad Hadi Hosseini, Yasin Abbasi-Yadkori, Sattar Vakili

benchmarks

arXiv Machine Learning

Jun 25

Minimax PAC Bounds for Learning in Exogenous Contextual MDPs

arXiv:2606. 25170v1 Announce Type: cross Abstract: We study PAC learning in tabular discounted Markov decision processes with exogenous i.

By Corentin Pla, Hugo Richard, Marc Abeille, Vianney Perchet

agents reinforcement-learning

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).

By Yang Xu, Swetha Ganesh, Vaneet Aggarwal

reinforcement-learning

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