I am familiar with supervised and unsupervised learning. I did the SaaS course done by Andrew Ng on Coursera.org.
I am looking for something similar for reinforcement learning.
Can you recommend something?
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To the good answers here, I would add
A brief overview of RL: Most essential concepts in one place.
Another brief overview, in presentation format.
Ben Recht's An outsider's tour of RL is pretty comprehensive and accessible.
The Bellman equations: central to the whole RL theory.
Policy gradients explained by Andrej Karpathy (mentioned in other answers as "pong from pixels", this is the link).
These barely scratch the surface of RL, but they should get you started.
A person who followed and finished the course wrote (as a Youtube comment):
Excellent course. Well paced, enough examples to provide a good intuition, and taught by someone who's leading the field in applying RL to games.
Before that ask yourself if you really want to learn about "reinforcement learning." Although there is much hype about reinforcement learning, the real-world applicability of reinforcement learning is almost non-existent. Most of the online courses teach you a very little about machine learning, so it is much better to get thorough with it, rather than proceeding towards reinforcement learning. Learning reinforcement learning is somewhat different from learning about unsupervised/supervised learning techniques.
Having said that, the fastest way to get a good grasp of reinforcement learning is as follow:
Watch Deep RL Bootcamp lectures.
To understand the math behind these techniques, refer to Sutton and Barto's Reinforcement Learning: An Introduction.
Read relevant papers (game-playing etc.).
P.S: Make sure that you are thorough with basics of neural networks, as most of the current papers in RL involve using DNNs in some or the other way as approximators.
I recently saw a course by Microsoft on edx. It is called 'Reinforcement Learning Explained'.
Here is the link: https://www.edx.org/course/reinforcement-learning-explained-0 This is not quite comprehensive but at least gives a good starting point.
I would say this post is a must to read: