Deep Learning of Robotic Tasks without a Simulator using Strong and Weak Human Supervision

Understand

We propose a scheme for training a computerized agent to perform complex human tasks such as highway steering.

• The scheme is designed to follow a natural learning process whereby a human instructor teaches a computerized trainee.
• The learning process consists of five elements: (i) unsupervised feature learning; (ii) supervised imitation learning; (iii) supervised reward induction; (iv) supervised safety module construction; and (v) reinforcement learning.
• We implemented the last four elements of the scheme using deep convolutional networks and applied it to successfully create a computerized agent capable of autonomous highway steering over the well-known racing game Assetto Corsa.