Coordinated Online Learning for Multi-Agent Systems with Coupled Constraints and Perturbed Utility Observations

• URL: http://arxiv.org/abs/2010.10878v1
• Date: Wed, 21 Oct 2020 10:11:17 GMT
• Title: Coordinated Online Learning for Multi-Agent Systems with Coupled Constraints and Perturbed Utility Observations
• Authors: Ezra Tampubolon and Holger Boche
• Abstract summary: We introduce a novel method to steer the agents toward a stable population state, fulfilling the given 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.
• Score: 91.02019381927236
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Competitive non-cooperative 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.

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