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Artificial Life and Robotics

, Volume 6, Issue 4, pp 200–204 | Cite as

Adaptive and economic data representation in control architectures of autonomous real-world robots

  • Joern Fischer
  • Ralph Breithaupt
  • Mathias Bode
Original Article

Abstract

Learning algorithms for autonomous robots in complex, real-world environments usually have to deal with many degrees of freedom and continuous state spaces. Reinforcement learning is a promising concept for unsupervised learning, but common algorithms suffer from huge storage and calculation requirements if they are used to construct an internal model by estimating a value-function for every action in every possible state. In our attempt to approximate this function at the lowest cost, we introduce a flexible method that focuses on the states of greatest interest, and interpolates between them with a fast and easy-to-implement algorithm. In order to provide the highest accuracy to any predefined limit, we enhanced this algorithm by a fast converging multilayer error approximator.

Keywords

Reinforcement Learning Associative Memory Vector Quantizer Voronoi Cell Function Approximator 
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

© ISAROB 2002

Authors and Affiliations

  1. 1.Fraunhofer Gesellschaft, Autonomous Intelligent SystemsAugustinGermany

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