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Practical Reinforcement Learning

Higher School of Economics via Coursera


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!

Do you have technical problems? Write to us:

Taught by

Pavel Shvechikov and Alexander Panin

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4.5 rating, based on 2 reviews

Start your review of Practical Reinforcement Learning

  • Abhilash Vj is taking this course right now, spending 8 hours a week on it and found the course difficulty to be medium.

    I have tried to follow CS294 from UC Berkely, tried watching David Silver lecture videos and John Schulman lectures and I struggled to understand the practical implementations of all those algorithms but this course we jump to a practical assignment after...
  • Francesco R completed this course, spending 9 hours a week on it and found the course difficulty to be medium.

    The course well deserves five, or even six, stars for offering this content. Despite the continue fanfares on media and SNS, RL and deep RL are almost never covered by MOOCs, and this course goes even beyond being a “notable exception”. The problems that...

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