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.
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.
arXiv:2606. 28943v1 Announce Type: cross Abstract: Learning to bid in repeated multi-unit auctions with bandit feedback poses a fundamental challenge.
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.
The paper introduces CURB, a reward‑shaping framework that penalizes the total variation distance between an agent’s action distributions under cooperation and defection histories, thereby preventing collusive equilibria in repeated games. By linking empirical Q‑learning collusion to Simple Penal Codes, the authors prove that any non‑trivial SPC can be neutralized, and demonstrate CURB’s effectiveness in both tabular and deep Q‑learning settings for Bertrand and Cournot competition.
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.
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.
The paper introduces online contextual matrix games, a framework that merges contextual bandits with multi‑player online games to handle dynamic contexts and strategic interactions. It presents OnGameLearn, an algorithm that balances exploration and exploitation across actions and contexts, providing statistical guarantees such as tail bounds, Nash equilibrium convergence, asymptotic normality, and sublinear regret. The authors also define a policy value for matrix games and propose a doubly robust, √T‑consistent estimator, demonstrating effectiveness through simulations and a hotel pricing case study.
The paper introduces state abstractions that preserve the difference of Q‑functions for offline reinforcement learning, aiming to exclude irrelevant dynamics from rich state data. It proposes a dynamic generalization of the R‑learner that uses orthogonal estimation and sparse learning to estimate the Q‑function contrast, achieving faster convergence and consistency under a margin condition. Experiments on simulated and simulator‑augmented real data show variance reductions and demonstrate that the necessary information for sequential decision‑making can be smaller than that required for full state prediction.
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.
arXiv:2510. 14642v2 Announce Type: replace-cross Abstract: In blockchain networks, the strategic ordering of transactions within blocks has emerged as a significant source of profit extraction, known as Maximal Extractable Value (MEV).
arXiv:2605. 29032v2 Announce Type: replace Abstract: Model-based reinforcement learning (MBRL) agents typically learn world models by minimizing predictive loss.
NashDreamer is a new model-based reinforcement learning framework designed for two-player zero-sum imperfect-information games. It introduces a centralized Multi-Agent Recurrent State-Space Model that separates environment dynamics from player strategy effects, enabling the use of any policy gradient algorithm while preserving convergence guarantees to Nash equilibria. Experiments on four benchmark games show that NashDreamer achieves significantly better sample efficiency than model-free baselines early in training, and the authors analyze its optimization landscape, noting a potential vulnerability to posterior collapse in stochastic settings.