Free course that takes you from zero to Reinforcement Learning PRO πŸ¦ΈπŸ»β€πŸ¦ΈπŸ½

Related tags

Deep Learning reinforcement-learning deep-reinforcement-learning
Overview

The Hands-on Reinforcement Learning course πŸš€

From zero to HERO πŸ¦ΈπŸ»β€πŸ¦ΈπŸ½

Out of intense complexities, intense simplicities emerge.

-- Winston Churchill

Contents

Welcome to the course πŸ€— ❀️

Welcome to my step by step hands-on-course that will take you from basic reinforcement learning to cutting-edge deep RL.

We will start with a short intro of what RL is, what is it used for, and how does the landscape of current RL algorithms look like.

Then, in each following chapter we will solve a different problem, with increasing difficulty:

  • πŸ† easy
  • πŸ† πŸ† medium
  • πŸ† πŸ† πŸ† hard

Ultimately, the most complex RL problems involve a mixture of reinforcement learning algorithms, optimizations and Deep Learning techniques.

You do not need to know deep learning (DL) to follow along this course.

I will give you enough context to get you familiar with DL philosophy and understand how it becomes a crucial ingredient in modern reinforcement learning.

Lectures

  1. Introduction to Reinforcement Learning
  2. Q-learning to drive a taxi πŸ†
  3. SARSA to beat gravity πŸ†
  4. Parametric Q learning to keep the balance πŸ’ƒ πŸ†
  5. Policy gradients to land on the Moon πŸ†

Wanna contribute?

There are 2 things you can do to contribute to this course:

  1. Spread the word and share it on Twitter, LinkedIn

  2. Open a pull request to fix a bug or improve the code readability.

Thanks ❀️

Special thanks to all the students who contributed with valuable feedback and pull requests ❀

Let's connect!

πŸ‘‰πŸ½ Subscribe to the datamachines newsletter.

πŸ‘‰πŸ½ Follow me on Medium, Twitter, LinkedIn

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Comments
  • Merge from AnthonyLapadula/hands-on-rl

    Merge from AnthonyLapadula/hands-on-rl

    Main changes:

    • Updated hparams in 03_cart_pole/notebooks/07_deep_q_agent_good_hyperparameters.ipynb (and left the old ones for comparison)
    • A few fixes for bugs that may only show up on my environment
      • Biggest changes were to get animations working
    • Fixed a few typos
    • Tweaks to .gitignore
    • Tried to clarify various README.md files, but probably failed
    • Removed a few unused imports to reduce required package set
    opened by AnthonyLapadula 1
  • Lapadula 149 threshold to halt search

    Lapadula 149 threshold to halt search

    Main change is to 03_cart_pole/notebooks/09_hyperparameter_search.ipynb. Added Optuna callback to stop hyperparameter search when the perfect mean reward is reached.

    Only other non-trivial change is to comment out hparams['nn_hidden_layers'] = eval(hparams['nn_hidden_layers']) in that same file.

    opened by AnthonyLapadula 0
Owner
Pau Labarta Bajo
Pau Labarta Bajo
GitHub http://datamachines.xyz/the-hands-on-reinforcement-learning-course/
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