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Karlruhe Brainstormers - Design Principles

Martin Riedmiller
University of Karlsruhe, ILKD, Germany

Sebastian Buck
University of Karlsruhe, ILKD, Germany

Sergio Dilger
University of Karlsruhe, ILKD, Germany

Ralf Ehrmann
University of Karlsruhe, ILKD, Germany

Artur Merke
University of Karlsruhe, ILKD, Germany

Ladda ner artikelhttp://www.ep.liu.se/ecp_article/index.en.aspx?issue=004;article=013

Ingår i: RobocCup-99 Team Descriptions. Simulation League

Linköping Electronic Conference Proceedings 4:13, s. 59-63

Linköping Electronic Articles in Computer and Information Science 6:13, s. 59-63

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Publicerad: 1999-12-15

ISBN:

ISSN: 1650-3686 (tryckt), 1650-3740 (online)

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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Referenser

[1] A. G. Barto; S. J. Bradtke; and S. P. Singh. Learning to act using realtime dynamic programming. Arti cial Intelligence; (72):81-138; 1995.

[2] M. Riedmiller. Autonomously learning neural controllers. VDI-Verlag; 1996. Dissertation University of Karlsruhe (in german).

[3] Peter Stone and Manuela Veloso. Team-partitioned; opaque-transition reinforcement learning. In M. Asada and H. Kitano; editors; RoboCup- 98: Robot Soccer World Cup II. Springer Buch Verlag; 1998.

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