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Distributed decision making in multi-agent networks has recently attractedsignificant research attention thanks to its wide applicability, e.g. in themanagement and optimization of computer networks, power systems, robotic teams,sensor networks and consumer markets. Distributed decision-making problems canbe modeled as inter-dependent optimization problems, i.e., multi-agentgame-equilibrium seeking problems, where noncooperative agents seek anequilibrium 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, weprovide an operator-theoretic characterization of convergence with atime-invariant communication network. For the time-varying case, we consideradjacency matrices that may switch subject to a dwell time. We illustrate ourinvestigations using a distributed robotic exploration example.

Towards time-varying proximal dynamics in Multi-Agent Network Games