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Recent research reveals that deep learning is an effective way of solvinghigh dimensional Hamilton-Jacobi-Bellman equations. The resulting feedbackcontrol law in the form of a neural network is computationally efficient forreal-time applications of optimal control. A critical part of this designmethod is to generate data for training the neural network and validating itsaccuracy. In this paper, we provide a survey of existing algorithms that can beused to generate data. All the algorithms surveyed in this paper arecausality-free, i.e., the solution at a point is computed without using thevalue of the function at any other points. At the end of the paper, anillustrative example of optimal feedback design using deep learning is given.

Algorithms of Data Development For Deep Learning and Feedback Design