arXiv Machine Learning August 14, 2026 By Larissa Xu, King Bi, William Chang

Decentralized Multi-Player Q-Learning in Episodic Markov Decision Processes with Information Asymmetry

Read the original on arXiv Machine Learning →

arXiv:2608. 12753v1 Announce Type: new Abstract: We study decentralized multi-player reinforcement learning in episodic tabular Markov decision processes (MDPs) under three forms of information asymmetry: (A) unobserved actions with common rewards, (B) observed actions with independent rewards, and (C) unobserved actions with independent rewards.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jul 23

Asymptotically Optimal Regret for Reinforcement Learning without Horizon Dependence

arXiv:2607. 19854v1 Announce Type: new Abstract: We study horizon-free regret minimization for finite-horizon time-homogeneous tabular Markov decision processes with $S$ states, $A$ actions, horizon $H$, and per-trajectory total reward bounded by $1$.

By Runlong Zhou, Zihan Zhang, Maryam Fazel, Simon S. Du

reinforcement-learning

arXiv Machine Learning

Jun 2

Online Learning in MDPs with Partially Adversarial Transitions and Losses

arXiv:2602. 09474v2 Announce Type: replace Abstract: We study reinforcement learning in MDPs whose transition function is stochastic at most steps but may behave adversarially at a fixed subset of $\Lambda$ steps per episode.

By Ofir Schlisselberg, Tal Lancewicki, Yishay Mansour

reinforcement-learning safety

arXiv Machine Learning

4d ago

Optimal Multi-Reward Reinforcement Learning

arXiv:2609.36486v1 Announce Type: new Abstract: We study an unknown-transition finite-horizon Markov decision process (MDP) with a finite collection of known reward functions $\{r^1, r^2, \ldots, r^M...

By Zijun Chen, Zihan Zhang

reinforcement-learning

arXiv Machine Learning

Jun 2

Fairness in two-player zero-sum games with bandit feedback

arXiv:2606. 01159v1 Announce Type: new Abstract: We study two-player zero-sum games (TPZSGs) with bandit feedback under fairness constraints requiring every action to be played with probability at least $\alpha/m$.

By S Akash, Pratik Gajane

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 Machine Learning

Aug 6

Sublogarithmic Swap Regret in Multiplayer General-Sum Games via Hybrid Regularization

arXiv:2608. 04149v1 Announce Type: cross Abstract: Swap regret governs the rate at which uncoupled learning dynamics converge to correlated equilibria in multiplayer general-sum games.

By Taira Tsuchiya

One email a morning, machine-written

Related stories

Asymptotically Optimal Regret for Reinforcement Learning without Horizon Dependence

Online Learning in MDPs with Partially Adversarial Transitions and Losses

Optimal Multi-Reward Reinforcement Learning

Fairness in two-player zero-sum games with bandit feedback

Minimax PAC Bounds for Learning in Exogenous Contextual MDPs

Sublogarithmic Swap Regret in Multiplayer General-Sum Games via Hybrid Regularization