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Aprendizaje de selección de acciones en un mundo simple pero impredecible
DOI:
https://doi.org/10.15446/ing.investig.n49.21404Keywords:
Aprendizaje por refuerzo, Aprendizaje Q, Agentes autónomos, Animats (es)Reinforcement learning, Q learning, Autonomous agents, Animats (en)
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Uno de los principales problemas estudiados en la simulación de agentes artificiales autónomos es el de la selección de acciones: un mecanismo que le permita al sistema escoger la acción más apropiada para la situación en que se encuentre, de tal forma que maximice su medida de éxito. El aprendizaje por refuerzo representa un enfoque atractivo para atacar este problema, ya que se basa en la búsqueda de señales de premio y la evasión de señales de castigo mediante un proceso de ensayo y error. En este artículo presentamos al PAISA I, una criatura artificial que aprende a comportarse (seleccionar acciones) utilizando una técnica de aprendizaje por refuerzo (aprendizaje Q) para optimizar la cantidad de comida que puede encontrar en un mundo impredecible, aunque con un espacio estado-acción pequeño.
One of the main problems studied in simulation of artificial autonomous agents is the action-selection: a mechanism that allows the system to choice the more suitable action for the specific situation where it is located, in such a way that maximizes his success measure. The reinforcement learning represents an attractive approach to attack this problem, because it is based in the searching of awards signals and the refusing of punishments by a trial and error process. In this paper, we present the PAISA I, an artificial creature that learns to behave (that is, action-selection) using a reinforcement learning technique known as Q-learning, to optimize the amount of food that he can find in an unpredictable world, although in a small state-action space.
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Copyright (c) 2002 Sergio A. Rojas, José J. Martínez
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