Towards time-varying proximal dynamics in Multi-Agent Network Games
This work addresses convergence guarantees for distributed decision-making in multi-agent systems with time-varying networks, which is relevant for applications like robotic teams and sensor networks, but the contribution is incremental as it extends existing operator-theoretic methods to a specific time-varying case.
The paper provides an operator-theoretic characterization of convergence for proximal dynamics in multi-agent network games, extending results to time-varying communication networks with switching adjacency matrices subject to a dwell time, and validates the approach with a distributed robotic exploration example.
Distributed decision making in multi-agent networks has recently attracted significant research attention thanks to its wide applicability, e.g. in the management and optimization of computer networks, power systems, robotic teams, sensor networks and consumer markets. Distributed decision-making problems can be modeled as inter-dependent optimization problems, i.e., multi-agent game-equilibrium seeking problems, where noncooperative agents seek an equilibrium by communicating over a network. To achieve a network equilibrium, the agents may decide to update their decision variables via proximal dynamics, driven by the decision variables of the neighboring agents. In this paper, we provide an operator-theoretic characterization of convergence with a time-invariant communication network. For the time-varying case, we consider adjacency matrices that may switch subject to a dwell time. We illustrate our investigations using a distributed robotic exploration example.