A Hierarchical Two-tier Approach to Hyper-parameter Optimization in Reinforcement Learning

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Optimization of hyper-parameters in real-world applications of reinforcement learning (RL) is a key issue, because their settings determine how fast the agent will learn its policy by interacting with its environment due to the information content of data gathered. In this work, an approach that use...

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Autores principales: Barsce, Juan Cruz, Palombarini, Jorge, Martínez, Ernesto
Formato: Articulo
Lenguaje:Inglés
Publicado: 2020
Materias:
Ciencias Informáticas
Reinforcement learning
hyper-parameter optimization
Bayesian optimization
Bayesian optimization of combinatorial structures (BOCS)
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/135049
https://publicaciones.sadio.org.ar/index.php/EJS/article/view/165
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata

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