Automatic tuning of the reinforcement function

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Description

The aim of this work is to present a method that helps tuning the reinforcement function parameters in a reinforcement learning approach. Since the proposal of neural based implementations for the reinforcement learning paradigm (which reduced learning time and memory requirements to realistic values) reinforcement functions have become the critical components. Using a general definition for reinforcement functions, the authors solve, in a particular case, the so called exploration versus exploitation dilemma through the careful computation of the RF parameter values. They propose an algorithm to compute, during the exploration part of the learning phase, an estimate for the parameter ... continued below

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11 p.

Creation Information

Touzet, C. & Santos, J.M. December 31, 1997.

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This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. More information about this article can be viewed below.

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Authors

  • Touzet, C. Oak Ridge National Lab., TN (United States). Center for Engineering Systems Advanced Research
  • Santos, J.M. Univ. de Buenos Aires (Argentina)

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Publisher

  • Oak Ridge National Laboratory
    Publisher Info: Oak Ridge National Lab., Center for Engineering Systems Advanced Research, TN (United States)
    Place of Publication: Tennessee

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Description

The aim of this work is to present a method that helps tuning the reinforcement function parameters in a reinforcement learning approach. Since the proposal of neural based implementations for the reinforcement learning paradigm (which reduced learning time and memory requirements to realistic values) reinforcement functions have become the critical components. Using a general definition for reinforcement functions, the authors solve, in a particular case, the so called exploration versus exploitation dilemma through the careful computation of the RF parameter values. They propose an algorithm to compute, during the exploration part of the learning phase, an estimate for the parameter values. Experiments with the mobile robot Nomad 200 validate their proposals.

Physical Description

11 p.

Notes

OSTI as DE98003165

Source

  • NEURAP `98, Marseille (France), 11-13 Mar 1998

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Identifier

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  • Other: DE98003165
  • Report No.: ORNL/CP--96046
  • Report No.: CONF-980338--
  • Grant Number: AC05-96OR22464
  • Office of Scientific & Technical Information Report Number: 292894
  • Archival Resource Key: ark:/67531/metadc675683

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Creation Date

  • December 31, 1997

Added to The UNT Digital Library

  • July 25, 2015, 2:20 a.m.

Description Last Updated

  • Nov. 3, 2016, 6:46 p.m.

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Touzet, C. & Santos, J.M. Automatic tuning of the reinforcement function, article, December 31, 1997; Tennessee. (digital.library.unt.edu/ark:/67531/metadc675683/: accessed September 22, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.