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Found 5 papers, 0 papers with code
Model predictive control guided with optimal experimental design for pulse-based parallel cultivation
Optimal experimental design for parameter precision attempts to maximize the information content in experimental data for a most effective identification of parametric model.
Automatic tuning of hyper-parameters of reinforcement learning algorithms using Bayesian optimization with behavioral cloning
In this work, in order to make an RL algorithm more user-independent, a novel approach for autonomous hyper-parameter setting using Bayesian optimization is proposed.
Generating Rescheduling Knowledge using Reinforcement Learning in a Cognitive Architecture
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 cognitive capabilities, similar to those found in human beings.
A Cognitive Approach to Real-time Rescheduling using SOAR-RL
Ensuring flexible and efficient manufacturing of customized products in an increasing dynamic and turbulent environment without sacrificing cost effectiveness, product quality and on-time delivery has become a key issue for most industrial enterprises.
Towards Autonomous Reinforcement Learning: Automatic Setting of Hyper-parameters using Bayesian Optimization
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 setting of hyper-parameter in learning algorithms is a key factor for achieving satisfactory performance regardless of user expertise in the inner workings of the techniques and methodologies.
