Brainstormers 2003 - Team Description
The main interest behind the Brainstormers' efiort in the robocup soccer domain is to develop and to apply machine learning tech- niques in complex domains. Especially, we are interested in reinforcement learning methods, where the training signal is only given in terms of suc- cess or failure. Our flnal goal is a learning system, where we only plug in 'win the match' - and our agents learn to generate the appropriate be- haviour. 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 difierent states and more than (1000) 300 difierent policies per agent per half time. This paper describes the new architecture of the Brainstormers team, the improved self-localization using particle fllters and the extensions of the learning algorithm to simultaniously learn with and without ball behaviors.