Facultad Regional Buenos Aires

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Author: search.filters.author.Gómez, Juan CarlosSubject: search.filters.subject.machine learning

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    Moving in a Simulated Environment Through Deep Reinforcement Learning
    (IEEE, 2022-09-08) Esarte, Javier; Folino, Pablo Daniel; Gómez, Juan Carlos
    Reinforcement learning is a field of artificial intelligence that is continuously evolving and has a wide variety of applications. In recent years major progress has been made in the application of deep reinforcement learning to highdimensional problems with continuous state and action spaces. This paper presents a complete analysis of the application of the soft actor-critic algorithm to teach a four legged robot with three joints on each leg how to move towards the center of a virtually simulated environment. The general formulation of the reinforcement learning problem is first presented, followed by the description of the environment under analysis and the applied algorithm. Afterwards, the obtained results are compared against those of a manually programmed policy, closing with a discussion of some key design choices and common challenges.
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    Predictor de deserción universitaria
    (2021-04-01) Romero, Giselle; Toranzo Calderón, Joaquin; Jaremczuk, Sebastián; Gómez, Juan Carlos; Verrastro, Claudio
    La deserción estudiantil siempre ha sido un tema de preocupación debido a sus múltiples implicancias. En este trabajo se propone la aplicación de técnicas de reconocimiento de patrones para exponer información útil y formular reglas de inferencia en sistemas de diagnóstico automático. De esta manera se generan modelos predictivos de deserción universitaria en la UTN.BA, a partir de bases de datos de estudiantes de la carrera de Ingeniería en Sistemas de la Información del plan K08. Se construyeron dos modelos, uno basado sobre Máquinas de Vectores de Soporte y otro sobre Redes neuronales. Ambos presentan resultados muy similares reconociendo a estudiantes en situación de deserción con una exactitud de 79%.