Research on team CGA learning based on stochastic game
Niu Liping · Computer Engineering and Applications Journal · 2009
In distributed virtual environment,through learning,individual CGA can adapt environment and other CGA in team,so the capability of team problems solving,the adaptability and robust of CGA team have been increased.When the learning based on random games of team CGA has much equilibrium,the equilibrium selection problem of every member in team must be solved.This paper gives a learning method for team CGA called TCCLA.It divides the learning into two levels:manager learning and non manager learning.Every member in team selects its optimization actions according to its preference.Non-manager learns the optimization equilibrium under the direction of manager.So the problem of equilibrium selection has been solved.The IPL algorithm has been improved.The efficiency of TCCLA has been validated through experimentation.