Konferensartikel

Between Teaching and Learning: Development of the Team Mainz Rolling Brains for the Simulation League of RoboCup 99

Daniel Polani
Johannes Gutenberg-Universität Mainz, Germany

Thomas Uthmann
Johannes Gutenberg-Universität Mainz, Germany

Ladda ner artikel

Ingår i: RobocCup-99 Team Descriptions. Simulation League

Linköping Electronic Conference Proceedings 4:18, s. 84-87

Linköping Electronic Articles in Computer and Information Science vol. 4 4:18, p. 84-87

Visa mer +

Publicerad: 1999-12-15

ISBN:

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

Abstract

The development of our team for RoboCup~99 is mainly oriented towards a transparent way of transferring explicit knowledge into the agent control and its combination with learning algorithms capable of fine-tuning the acquired skills. The explicit knowledge is formulated in terms of rules; the non-explicit knowledge is to be realized as a set of parameters adapted by hierarchical reinforcement learning and by rule evolution. The teaching process for the implicit learning is not determined by a simple fixed reinforcement return; but by a --- possibly complex --- agent that represents a human or an automated coach.

Nyckelord

Inga nyckelord är tillgängliga

Referenser

[1] T. G. Dietterich. Hierarchical reinforcement learning with the MAXQ value function decomposition. Submitted to Machine Learning; 1999.

[2] G. Edelman. Neural Darwinism: Theory of Neuronal Group Selection. Basic Books; New York; 1987.

[3] Leslie Pack Kaelbling; Michael L. Littman; and Andrew W. Moore. Reinforcement learning: a survey. Journal of Arti cial Intelligence Research; 4:237-285; May 1996.

[4] Hitoshi Matsubara; Itsuki Noda; and Kazuo Hiraki. Learning of cooperative actions in multi-agent systems: a case study of pass in soccer. In AAAI-96 Spring Symposium on Adaptation; Coevolution and Learning in Multi-Agent Systems; pages 63-67; Mar 1996.

[5] D. Moriarty and R. Miikkulainen. Forming neural networks through ecient and adaptive co-evolution. Evolutionary Computation; 5:373- 399; 1997.

[6] D. Polani and R. Miikkulainen. Fast reinforcement learning through eugenic neuro-evolution. Technical Report AI99-277; Department of Computer Sciences; The University of Texas at Austin; January 1999.

[7] R. S. Sutton and A. G. Barto. Reinforcement Learning. Bradford; 1998.

Citeringar i Crossref