Learning obstacle avoidance with an operant behavior model.
Artif Life
; 10(1): 65-81, 2004.
Article
em En
| MEDLINE
| ID: mdl-15035863
Artificial intelligence researchers have been attracted by the idea of having robots learn how to accomplish a task, rather than being told explicitly. Reinforcement learning has been proposed as an appealing framework to be used in controlling mobile agents. Robot learning research, as well as research in biological systems, face many similar problems in order to display high flexibility in performing a variety of tasks. In this work, the controlling of a vehicle in an avoidance task by a previously developed operant learning model (a form of animal learning) is studied. An environment in which a mobile robot with proximity sensors has to minimize the punishment for colliding against obstacles is simulated. The results were compared with the Q-Learning algorithm, and the proposed model had better performance. In this way a new artificial intelligence agent inspired by neurobiology, psychology, and ethology research is proposed.
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Base de dados:
MEDLINE
Assunto principal:
Aprendizagem da Esquiva
/
Redes Neurais de Computação
/
Condicionamento Operante
/
Modelos Psicológicos
Idioma:
En
Revista:
Artif Life
Assunto da revista:
BIOLOGIA
Ano de publicação:
2004
Tipo de documento:
Article
País de afiliação:
Argentina