Planning and Control
This lecture covers the concept of model predictive control and is a part of the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this module include: Models 1-6 of this course and an Introduction to Data Science or a Graduate Level Machine Learning course.
Chapters:
00:00 – Background on the class creation
00:58 – Take a quiz!
01:33 – Planning and control
02:21 – Action plan (table of contents)
09:06 – State transition equations
31:22 – A few numerical examples (I), no action
34:44 – A few numerical examples (II), negative acceleration
36:24 – A few numerical examples (II), positive and negative steering
38:31 – PyTorch implementation of physical examples
46:26 – Kelley-Bryson algorithm (RNN recap and control)
56:29 – Control with final cost
59:13 – Control with cumulative cost
1:00:42 – PyTorch implementation of optimal control examples
