In this short course, we will study how to incorporate elements of machine learning into optimal control design. The course is split into 3 parts:
1 Fundamentals of optimal control: dynamic optimization, linear-quadratic control, dynamic programming and Pontryagin's maximum principle. Nonlinear optimal control.
2 Approximation methods in high dimensions: polynomial approximation, deep neural networks. Optimization techniques: LASSO regression, stochastic gradient descent, training neural networks.
3 Synthetic data-driven schemes for optimal control. Combining the first two parts, we will study the construction of data-driven schemes for the approximation of high-dimensional nonlinear control laws. Examples in Matlab.
The schedule of the course is:
Students interested are invited to contact Prof. Albi (email@example.com)
Strada le Grazie 15
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