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Online Course

Practical Reinforcement Learning

Higher School of Economics via Coursera

Overview

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: coursera@hse.ru

Syllabus

Intro: why should I care?
-In this module we are gonna define and "taste" what reinforcement learning is about. We'll also learn one simple algorithm that can solve reinforcement learning problems with embarrassing efficiency.

At the heart of RL: Dynamic Programming
-This week we'll consider the reinforcement learning formalisms in a more rigorous, mathematical way. You'll learn how to effectively compute the return your agent gets for a particular action - and how to pick best actions based on that return.

Model-free methods
-This week we'll find out how to apply last week's ideas to the real world problems: ones where you don't have a perfect model of your environment.

Approximate Value Based Methods
-This week we'll learn to scale things even farther up by training agents based on neural networks.

Policy-based methods
-We spent 3 previous modules working on the value-based methods: learning state values, action values and whatnot. Now's the time to see an alternative approach that doesn't require you to predict all future rewards to learn something.

Exploration
-In this final week you'll learn how to build better exploration strategies with a focus on contextual bandit setup. In honor track, you'll also learn how to apply reinforcement learning to train structured deep learning models.

Taught by

Pavel Shvechikov and Alexander Panin

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Reviews

4.5 rating, based on 2 reviews

Start your review of Practical Reinforcement Learning

  • 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...
  • 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...

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