Reinforcement Learning, Apprenticeship Learning and Robotic Control

author: Andrew Ng, Computer Science Department, Stanford University
published: Aug. 26, 2009,   recorded: June 2009,   views: 9621

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


Reinforcement learning has proved to be a powerful method for robotic control. In this talk, drawing on examples from autonomous helicopter flight, quadruped robot control and autonomous driving, I'll describe some of the challenges we've faced in applying RL algorithms to various control problems, such as (i) Problems where the reward function is exceedingly difficult to specify by hand, and must itself be learned, (ii) Safe exploration, where one wishes to explore without damaging the robot, and (iii) Learning high performance controllers even if we have only an extremely inaccurate model of our robot's dynamics. Using apprenticeship learning - in which we learn by watching an expert demonstration - as a unifying theme, I'll also describe a few algorithms for addressing these challenges.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: