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A Self-Made Agent Based on Action-Selection

Identifieur interne : 007A94 ( Main/Merge ); précédent : 007A93; suivant : 007A95

A Self-Made Agent Based on Action-Selection

Auteurs : Olivier Buffet ; Alain Dutech

Source :

RBID : CRIN:buffet03b

English descriptors

Abstract

Some agents have to face multiple objectives simultaneously. In such cases, and considering partially observable environments, classical Reinforcement Learning (RL) is prone to fall in pretty low local optima, only learning straightforward behaviors. We present here a method that tries to identify and learn independent ``basic'' behaviors solving separate tasks the agent has to face. Using a combination of these behaviors (an action-selection algorithm), the agent is then able to efficiently deal with various complex goals in complex environments.

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CRIN:buffet03b

Le document en format XML

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<title xml:lang="en" wicri:score="274">A Self-Made Agent Based on Action-Selection</title>
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<date when="2003" year="2003">2003</date>
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<term>action selection</term>
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<div type="abstract" xml:lang="en" wicri:score="2344">Some agents have to face multiple objectives simultaneously. In such cases, and considering partially observable environments, classical Reinforcement Learning (RL) is prone to fall in pretty low local optima, only learning straightforward behaviors. We present here a method that tries to identify and learn independent ``basic'' behaviors solving separate tasks the agent has to face. Using a combination of these behaviors (an action-selection algorithm), the agent is then able to efficiently deal with various complex goals in complex environments.</div>
</front>
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{{Explor lien
   |wiki=    Wicri/Lorraine
   |area=    InforLorV4
   |flux=    Main
   |étape=   Merge
   |type=    RBID
   |clé=     CRIN:buffet03b
   |texte=   A Self-Made Agent Based on Action-Selection
}}
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