Coordinated Online Learning for Multiagent Systems With Coupled Constraints and Perturbed Utility Observations

• 成果类型: Article
• 署名作者: Tampubolon, Ezra; Boche, Holger
• 署名单位: Technical University of Munich; Ruhr University Bochum
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.3034874
• 发表日期: 2021
• 页码: 5080-5095
• 关键词: Sociology; statistics; Nash equilibrium; games; mirrors; pricing; CONVERGENCE; Agents and autonomous systems; Constrained control; game theory; Machine Learning

摘要

Competitive noncooperative online decision-making agents whose actions increase congestion of scarce resources constitute a model for widespread modern large-scale applications. To ensure sustainable resource behavior, we introduce a novel method to steer the agents toward a stable population state, fulfilling the given coupled resource constraints. The proposed method is a decentralized resource pricing method based on the resource loads resulting from the augmentation of the game's Lagrangian. Assuming that the online learning agents have only noisy first-order utility feedback, we show that for a polynomially decaying agents step size/learning rate, the population's dynamic will almost surely converge to generalized Nash equilibrium. A particular consequence of the latter is the fulfillment of resource constraints in the asymptotic limit. Moreover, we investigate the finite-time quality of the proposed algorithm by giving a nonasymptotic time decaying bound for the expected amount of resource constraint violation.