O'Reilly logo

Game Theory and Learning for Wireless Networks by Hamidou Tembine, Samson Lasaulce

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Chapter 6. Fully Distributed Learning Algorithms
In fully distributed learning algorithms, we assume that the players use less information about other players and the history of the game. It is not always apparent that these problems can be formulated as a game, so the general game model for fully distributed learning is presented.
Learning by experimentation and trial-and-error can come close to pure Nash equilibrium play under certain conditions.
Reinforcement learning is where players relate their utility to actions previously taken, to optimize future rewards.
Regret minimization, and Boltzmann-Gibbs learning algorithms, are also considered, where the maxima and equilibria are approached after a reasonably small number of iterations.
In the schemes ...

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required