Fully distributed Nash equilibrium seeking over time-varying communication networks with linear convergence rate

10 Sep 2020 Bianchi Mattia Grammatico Sergio

We design a distributed algorithm for learning Nash equilibria over time-varying communication networks in a partial-decision information scenario, where each agent can access its own cost function and local feasible set, but can only observe the actions of some neighbors. Our algorithm is based on projected pseudo-gradient dynamics, augmented with consensual terms... (read more)

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Categories


  • OPTIMIZATION AND CONTROL
  • COMPUTER SCIENCE AND GAME THEORY
  • MULTIAGENT SYSTEMS