Distributed Nash Equilibrium Seeking in Consistency-Constrained Multi-Coalition Games

TL;DR

This paper introduces a distributed algorithm for finding Nash equilibria in multi-coalition games with internal agreement constraints, accommodating unbalanced networks and unifying various game and optimization frameworks.

Contribution

It proposes a novel iterative method for NE seeking in multi-coalition games with internal agreement, extending existing models to unbalanced networks and unifying multiple problem types.

Findings

Linear convergence of the proposed algorithm

Algorithm accommodates unbalanced network topologies

Unifies networked games and distributed optimization

Abstract

Distributed Nash equilibrium (NE) seeking problem for multi-coalition games has attracted increasing attention in recent years, but the research mainly focuses on the case without agreement demand within coalitions. This paper considers a class of networked games among multiple coalitions where each coalition contains multiple agents that cooperate to minimize the sum of their costs, subject to the demand of reaching an agreement on their state values. Furthermore, the underlying network topology among the agents does not need to be balanced. To achieve the goal of NE seeking within such a context, two estimates are constructed for each agent, namely, an estimate of partial derivatives of the cost function and an estimate of global state values, based on which, an iterative state updating law is elaborately designed. Linear convergence of the proposed algorithm is demonstrated. It is shown that the consistency-constrained multi-coalition games investigated in this paper put the well-studied networked games among individual players and distributed optimization in a unified framework, and the proposed algorithm can easily degenerate into solutions to these problems.