The Truck Backer-Upper
This lecture covers the concepts of emulation of kinematics from observations and training a policy. It 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 – Welcome to class
00:00:28 – Action plan
00:05:05 – State transition equations (recap)
00:07:13 – The Truck Backer-Upper
00:09:16 – Vehicle configuration
00:11:47 – Implementation in a Jupyter Notebook
00:13:52 – Manual parking tests
00:18:03 – Training: a two-stage learning process
00:20:57 – State update equations for a trailer truck
00:22:59 – Emulator training strategy
00:26:27 – Training protocol (I)
00:31:14 – Control as RNN (again)
00:35:37 – Training protocol (II)
00:38:16 – Unrolling in time (AKA BPTT)
00:39:59 – Successful controller's trajectories
00:42:03 – Additional resources
00:42:56 – PyTorch (partial) implementation
00:50:32 – Bayesian neural nets
00:53:34 – Dropout
00:55:59 – Uncertainty for a regressor (demo)
01:00:35 – And that was it :D
