| Abstract: |
The following paper describes the design principles of the
Karlruhe Brainstormers team for the RoboCup Simulator League. The basic
motivation behind our approach is to broadly apply Machine Learning techniques.
In particular, our longterm goal is to apply Reinforcement Learning techniques
to autonomously learn team playing capabilities. This longterm goal determined
the structure of the decision module, which has to choose between several
available 'high-level' moves based on evaluation functions. We plan to reach
the final autonomously learning agent in several stages. The current version
uses a hybrid decision module with both rule-based and learning components.
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