Distributed Strategic Learning for Wireless Engineers

7

Learning under Random Updates

7.1    Introduction

In a situation such as a dynamic environment under uncertainty, one would like to have a learning and adaptive procedure that does not require any information about the other players’ actions or payoffs, and less memory (a small number of parameters in terms of past own-experiences and observed data) as possible. In the previous chapters, we have called such a rule fully distributed. In a dynamic unknown environment, such fully distributed learning schemes are essential for applications of game theory and distributed optimization. In dynamic scenarios with

•  a random set of active players, and where

•  the traffic, the topology, and the states of the environment may vary over time and the ...