Brainstormers 2004-Team Description
The main interest behind the Brainstormers’ effort in the robocup soccer domain is to develop and to apply machine learning techniques in complex domains. Especially, we are interested in reinforcement learning methods, where the training signal is only given in terms of success or failure. Our final goal is a learning system, where we only plug in ’win the match’ and our agents learn to generate the appropriate behaviour. Unfortunately, even from very optimistic complexity estimations it becomes obvious, that in the soccer domain, both conventional solution techniques and also advanced today’s reinforcement learning techniques come to their limit there are more than (108×50)23 different states and more than (1000) different policies per agent per half time. This paper describes the new architecture of the Brainstormers team, the improved self-localization using particle filters and the extensions of the learning algorithm to simultaniously learn with and without ball behaviors.