Model-based vs model-free - Practical Reinforcement Learning

Link to this course:
Model-based vs model-free - Practical Reinforcement Learning
Advanced Machine Learning Specialization
Welcome to the Reinforcement Learning course.

Here you will find out about:

- foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc.
--- with math & batteries included

- using deep neural networks for RL tasks
--- also known as the hype train

- state of the art RL algorithms
--- and how to apply duct tape to them for practical problems.

- and, of course, teaching your neural network to play games
--- because that's what everyone thinks RL is about. We'll also use it for seq2seq and contextual bandits.

Jump in. It's gonna be fun!

A great course with very practical assignments to help you learn how to implement RL algorithms. But it also has some stupid quiz questions which makes you feel confusing.,Challenging (unlike many other courses on Coursera, it does not baby you and does not seem to be targeting as high a pass rate as possible), but very very rewarding.

Model-based vs model-free - Practical Reinforcement Learning
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