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A Self-Made Agent Based on Action-Selection

Olivier Buffet 1 Alain Dutech 1
1 MAIA - Autonomous intelligent machine
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
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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Submitted on : Tuesday, September 26, 2006 - 9:41:36 AM
Last modification on : Friday, February 26, 2021 - 3:28:05 PM


  • HAL Id : inria-00099828, version 1



Olivier Buffet, Alain Dutech. A Self-Made Agent Based on Action-Selection. Sixth European Workshop on Reinforcement Learning - EWRL-6 2003, 2003, Nancy, France, pp.47-48. ⟨inria-00099828⟩



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