arXiv Machine Learning June 5, 2026 By Qintong Xie, Edward Koh, Xavier Cadet, Peter Chin

DNQ: Deep Nash Q-Network for Partially Observable n-Player Games

Read the original on arXiv Machine Learning →

arXiv:2606. 06480v1 Announce Type: cross Abstract: Many real-world competitive systems require multiple decision-makers to act simultaneously under shared constraints, limited information, and repeated interaction, as in auctions, resource allocation, and security competition.

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

agents

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

Jun 30

A3M: Adaptive, Adversarial and Multi-Objective Learning for Strategic Bidding in Repeated Auctions

arXiv:2606. 28943v1 Announce Type: cross Abstract: Learning to bid in repeated multi-unit auctions with bandit feedback poses a fundamental challenge.

By Junhan Li, Yuxin Zhang, Haoran Wang, Minghao Chen

reinforcement-learning benchmarks 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

Aug 4

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria

arXiv:2510. 18183v3 Announce Type: replace Abstract: Finding Nash equilibria in two-player zero-sum imperfect-information games remains a central challenge in multi-agent reinforcement learning.

By Eason Yu, Tzu Hao Liu, Cl\'ement L. Canonne, Yunke Wang, Chang Xu, Nguyen H. Tran, Stefano V. Albrecht

agents reinforcement-learning benchmarks

arXiv Machine Learning

1d ago

BRAID: Learning Equilibrium Maps in Interdependent Security Games via Weight-Tied Iterative Graph Neural Networks

arXiv:2608. 14856v1 Announce Type: cross Abstract: Computing Nash equilibria in interdependent security (IDS) games on networks is computationally expensive: best-response dynamics may need hundreds of iterations per instance, and downstream tasks such as auditing, stress-testing, and incentive design often require repeatedly re-solving the game under parameter perturbations.

By Elnaz Nowrouzi, Zhiqun Zuo, Xueru Zhang, Mohammad Mahdi Khalili

arXiv AI

Jul 22

Comparative Study of Multi-Agent Actor-Critic Algorithms in Parameterized Action Reinforcement Learning

arXiv:2607. 19117v1 Announce Type: new Abstract: Parameterized action reinforcement learning has shown strong performance in environments requiring both discrete action selection and continuous parameterization.

By Ubayd Ali Bapoo, Clement N Nyirenda

agents reinforcement-learning benchmarks

Related stories

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

A3M: Adaptive, Adversarial and Multi-Objective Learning for Strategic Bidding in Repeated Auctions

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

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria

BRAID: Learning Equilibrium Maps in Interdependent Security Games via Weight-Tied Iterative Graph Neural Networks

Comparative Study of Multi-Agent Actor-Critic Algorithms in Parameterized Action Reinforcement Learning