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

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 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

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

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

arXiv Machine Learning

Sep 10

Local and Global Stability in Performative Reinforcement Learning

arXiv:2609.06467v1 Announce Type: new Abstract: In performative reinforcement learning the deployed policy shapes the environment that generates the learner's future data, and the natural solution co...

By Debmalya Mandal

agents reinforcement-learning

arXiv Machine Learning

Jun 16

Learning Policy from a Single Trajectory in Average-Reward Markov Decision Process

arXiv:2606. 16729v1 Announce Type: new Abstract: While there is an extensive body of work characterizing the sample complexity of discounted cumulative-reward MDPs, finite sample analyses for average-reward MDPs have been limited, and most existing works rely on restrictive assumptions such as ergodicity or access to a generative model.

By Jongmin Lee, Ernest K. Ryu, Vaneet Aggarwal

reinforcement-learning

arXiv Machine Learning

2d ago

High-Probability Nash Regret for Decentralized Learning in Markov $\alpha$-Potential Games: Episodic and Fully Online Asynchronous Algorithms with Applications to Markov Congestion Games

arXiv:2609. 14959v1 Announce Type: new Abstract: We study decentralized learning of Nash equilibria (NE) in infinite-horizon discounted Markov games under bandit feedback, focusing on Markov $\alpha$-potential games.

By S. Rasoul Etesami

reinforcement-learning safety

arXiv Machine Learning

Aug 18

Near-Optimal Sample Complexity Bounds for Constrained Average-Reward MDPs

arXiv:2509. 16586v2 Announce Type: replace Abstract: Recent advances have significantly improved our understanding of the sample complexity of learning in average-reward Markov decision processes (AMDPs) under the generative model.

By Yukuan Wei, Xudong Li, Lin F. Yang

reinforcement-learning safety

arXiv Machine Learning

Aug 11

Regret, equilibrium, and learning in games: A guided tour

arXiv:2608. 09389v1 Announce Type: cross Abstract: This note aims to serve as an entry point to the literature on learning in games, a topic with significant theoretical appeal and a wide range of applications -- from machine learning and data science to economics and beyond.

By Panayotis Mertikopoulos

agents reinforcement-learning safety

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