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In this paper, we consider a learning problem among non-cooperative agentsinteracting in a time-varying system. Specifically, we focus on repeated linearquadratic network games, in which the network of interactions changes with timeand agents may not be present at each iteration. To get tractability, we assumethat at each iteration, the network of interactions is sampled from anunderlying random network model and agents participate at random with a givenprobability. Under these assumptions, we consider a gradient-based learningalgorithm and establish almost sure convergence of the agents' strategies tothe Nash equilibrium of the game played over the expected network.Additionally, we prove, in the large population regime, that the learnedstrategy is an $\epsilon$-Nash equilibrium for each stage game with highprobability. We validate our results over an online market application.

Gradient Dynamics in Linear Quadratic Network Games with Time-Varying Connectivity and Population Fluctuation