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Practical Reinforcement Learning

Higher School of Economics via Coursera

2 Reviews 55 students interested
  • Provider Coursera
  • Subject Artificial Intelligence
  • Cost Free Online Course (Audit)
  • Session Upcoming
  • Language English
  • Certificate Paid Certificate Available
  • Start Date
  • Duration 6 weeks long
  • Learn more about MOOCs

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Overview

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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!

Taught by

Pavel Shvechikov and Alexander Panin

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Reviews for Coursera's Practical Reinforcement Learning
4.5 Based on 2 reviews

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  • 1
Francesco R
4.0 9 months ago
by Francesco 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 have been prepared and the assignments based on OpenAI gym are really challenging and entertaining. “Practical” is really a proper attribute of this course, and this does not subtract to the quality of content, as the lecturers provided plenty of links to state-of-the-art techniques - and many assignments make use of discoveries that are just…
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Abhilash V
5.0 11 months ago
by Abhilash 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 most lectures and that helped me gain a practical sense of all that is taught and kept me heavily motivated. I binge watched the videos and did programming assignments the weekend I got access to the course. I think this course can be what Andrew Ng's course is for machine learning to Reinforcement  learning.

This course have hon…
Was this review helpful to you? Yes
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