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Self-Growth of Basic Behaviors
in an Action Selection Based Agent AbstractWe investigate on designing agents facing multiple objectives simultaneously, that creates dif- cult situations, even if each objective is of low complexity. The present paper builds on an existing action selection process based on basic behaviors (resulting in a modular architecture) and proposes an algorithm for automatically selecting and learning the required basic behaviors through an incremental Reinforcement Learning approach. This leads to a very autonomous architecture, as the hand-coding is here reduced to its minimum.
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