arXiv AI July 14, 2026 By Naman Aggarwal, Jonathan P. How

Beyond Bayesian Nash: Learning Minimax-Regret Equilibria for Adversarial Team Games under Asymmetric Information

arXiv:2607. 09993v1 Announce Type: cross Abstract: Adversarial team games (ATGs) with asymmetric information, such as adversarial path-finding, goal search, and reachability games on graphs, require strategies that are robust to hidden opponent types, such as a hidden goal flag, and to deception.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 29

Conservative Equilibrium Discovery in Offline Game-Theoretic Multiagent Reinforcement Learning

arXiv:2603. 00374v2 Announce Type: replace Abstract: Offline learning of strategies takes data efficiency to its extreme by restricting algorithms to a fixed dataset of state-action trajectories.

By Austin A. Nguyen, Michael P. Wellman

reinforcement-learning benchmarks

arXiv Machine Learning

Jun 11

Phi-Actor-Critic: Steering General-Sum Games to Pareto-Efficient Correlated Equilibria

arXiv:2606. 11284v1 Announce Type: cross Abstract: Real-world multi-agent systems, from traffic coordination to resource allocation, are often modeled as general-sum games where individual incentives conflict with collective welfare.

By Wongyu Lee, Francesco Lelli, Omran Ayoub, Massimo Tornatore

agents 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

arXiv Machine Learning

Jul 10

Wasserstein Distributionally Robust Regret Optimization for Reinforcement Learning from Human Feedback

arXiv:2605. 00155v3 Announce Type: replace Abstract: Reinforcement learning from human feedback (RLHF) is a central post-training tool for aligning large language models, but its training reward is only a learned proxy for true human utility.

By Yikai Wang, Shang Liu, Jose Blanchet

llms reinforcement-learning

arXiv Machine Learning

Jun 5

Regret Minimization with Adaptive Opponents in Repeated Games

arXiv:2606. 06486v1 Announce Type: new Abstract: In this paper, we study regret minimization in repeated games with \emph{adaptive} opponents who can respond based on histories of play.

By Mingyang Liu, Asuman Ozdaglar, Tiancheng Yu, Kaiqing Zhang

arXiv AI

23h ago

Quantifying Risk Under Evolving Uncertainty: Belief-Dependent Robustness for Safe Sequential Decision Making

arXiv:2608. 17574v1 Announce Type: new Abstract: How cautious should an agent be while it is still learning its environment?

By Deep Kumar Ganguly, Jan Kretinsky

llms agents safety

Related stories

Conservative Equilibrium Discovery in Offline Game-Theoretic Multiagent Reinforcement Learning

Phi-Actor-Critic: Steering General-Sum Games to Pareto-Efficient Correlated Equilibria

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

Wasserstein Distributionally Robust Regret Optimization for Reinforcement Learning from Human Feedback

Regret Minimization with Adaptive Opponents in Repeated Games

Quantifying Risk Under Evolving Uncertainty: Belief-Dependent Robustness for Safe Sequential Decision Making