Algorithms of Data Development For Deep Learning and Feedback Design

Understand

Recent research reveals that deep learning is an effective way of solving high dimensional Hamilton-Jacobi-Bellman equations.

• The resulting feedback control law in the form of a neural network is computationally efficient for real-time applications of optimal control.
• A critical part of this design method is to generate data for training the neural network and validating its accuracy.
• In this paper, we provide a survey of existing algorithms that can be used to generate data.