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Abstract

The last chapter was intended to show the variety of problems in which ACS2 is able to learn. Model learning results have been provided in four different tasks showing that ACS2 is indeed capable of forming a generalized, complete model of an environment. Genetic generalization showed to be able to remedy the possibly occurring over-specializations in the ALP. The result was a decrease in population size and model size and a consequent decrease in computation time and the evolution of an even better environmental model. Moreover, the reinforcement learning capabilities of ACS2 were shown in the multiplexer and the maze environment.

Keywords

Reinforcement Learning Environmental Model Model Size Effect Part Environmental Niche 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • Martin V. Butz
    • 1
  1. 1.University of WürzburgGermany

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