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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.
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By HackerNoonThis 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.