Search Results for author:
Found 6 papers, 0 papers with code
Welfare Measure for Resource Allocation with Algorithmic Implementation: Beyond Average and Max-Min
In this work, we propose an axiomatic approach for measuring the performance/welfare of a system consisting of concurrent agents in a resource-driven system.
On Information Asymmetry in Competitive Multi-Agent Reinforcement Learning: Convergence and Optimality
In this work, we study the system of interacting non-cooperative two Q-learning agents, where one agent has the privilege of observing the other's actions.
Coordinated Online Learning for Multi-Agent Systems with Coupled Constraints and Perturbed Utility Observations
Competitive non-cooperative online decision-making agents whose actions increase congestion of scarce resources constitute a model for widespread modern large-scale applications.
Robust Online Learning for Resource Allocation -- Beyond Euclidean Projection and Dynamic Fit
Online-learning literature has focused on designing algorithms that ensure sub-linear growth of the cumulative long-term constraint violations.
Semi-Decentralized Coordinated Online Learning for Continuous Games with Coupled Constraints via Augmented Lagrangian
We give a condition on the step sizes and the degree of the augmentation of the Lagrangian, such that the proposed algorithm converges to a generalized Nash equilibrium.
Pricing Mechanism for Resource Sustainability in Competitive Online Learning Multi-Agent Systems
In case that the noise is persistent, and for several choices of the intrinsic parameter of the agents, such as their learning rate, and of the mechanism parameters, such as the learning rate of -, the progressivity of the price-setters, and the extrinsic price sensitivity of the agents, we show that the accumulative violation of the resource constraints of the resulted iterates is sub-linear w. r. t.
