4 papers
A Hierarchical Two-tier Approach to Hyper-parameter Optimization in Reinforcement Learning
Juan Cruz Barsce, Jorge A. Palombarini, Ernesto Martínez
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 environ…
Generating Rescheduling Knowledge using Reinforcement Learning in a Cognitive Architecture
Jorge A. Palombarini, Juan Cruz Barsce, Ernesto C. Martínez
In order to reach higher degrees of flexibility, adaptability and autonomy in manufacturing systems, it is essential to develop new rescheduling methodologies which resort to cogni…
A Cognitive Approach to Real-time Rescheduling using SOAR-RL
Juan Cruz Barsce, Jorge A. Palombarini, Ernesto C. Martínez
Ensuring flexible and efficient manufacturing of customized products in an increasing dynamic and turbulent environment without sacrificing cost effectiveness, product quality and…
Towards Autonomous Reinforcement Learning: Automatic Setting of Hyper-parameters using Bayesian Optimization
Juan Cruz Barsce, Jorge A. Palombarini, Ernesto C. Martínez
With the increase of machine learning usage by industries and scientific communities in a variety of tasks such as text mining, image recognition and self-driving cars, automatic s…