A Consensus-Based Algorithm for Non-Convex Multiplayer Games: Abstract and Introduction

This story was originally published on HackerNoon at: https://hackernoon.com/a-consensus-based-algorithm-for-non-convex-multiplayer-games-abstract-and-introduction. A novel algorithm using swarm intelligence to find global Nash equilibria in nonconvex multiplayer games, with convergence guarantees and numerical experiments. Check more stories related to gaming at: https://hackernoon.com/c/gaming. You can also check exclusive content about #games, #numerical-experiments, #consensus-based-optimization, #zeroth-order-algorithm, #nonconvex-multiplayer-games, #global-nash-equilibria, #metaheuristics, #mean-field-convergence, and more. This story was written by: @oligopoly. Learn more about this writer by checking @oligopoly's about page, and for more stories, please visit hackernoon.com. In this paper, we present a novel consensus-based zeroth-order algorithm tailored for nonconvex multiplayer games. The proposed method leverages a metaheuristic approach using concepts from swarm intelligence to reliably identify global Nash equilibria. We utilize a group of interacting particles, each agreeing on a specific consensus point, asymptotically converging to the corresponding optimal strategy.