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

Mostrar todas las versiones(2)

Optimization of hyper-parameters in reinforcement learning (RL) algorithms is a key task, because they determine how the agent will learn its policy by interacting with its environment, and thus what data is gathered. In this work, an approach that uses Bayesian optimization to perform a two-step op...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Barsce, Juan Cruz, Palombarini, Jorge, Martínez, Ernesto
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2019
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/10915/87851
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata

Ejemplares similares

Ejemplares similares