/Deep_reinforcement_learning_Course

Notebooks from our series of blogpost about Deep Reinforcement Learning

Primary LanguageJupyter Notebook

Deep Reinforcement Learning Course

This notebook is part of the Free Deep Reinforcement Course 📝

Deep Reinforcement Course

Deep Reinforcement Learning Course is a free series of blog posts about Deep Reinforcement Learning, where we'll learn the main algorithms, and how to implement them in Tensorflow.

The goal of these articles is to explain step by step from the big picture and the mathematical details behind it, to the implementation with Tensorflow

Syllabus

Part 1: Introduction to Reinforcement Learning ARTICLE

Part 2: Q-learning with FrozenLake ARTICLE // FROZENLAKE IMPLEMENTATION

Part 3: Deep Q-learning with Doom ARTICLE // DOOM IMPLEMENTATION

Part 3+: Improvments in Deep Q-Learning [ARTICLE (APRIL)] // [DOOM IMPLEMENTATION (04/12/2018)]

Part 4: Policy Gradients with Doom [ARTICLE (APRIL)] // [DOOM IMPLEMENTATION (04/23/2018)]

Part 5: Part 5: Asynchronous Advantage Actor Critic [ARTICLE (APRIL)] // [SUPER MARIO BROS IMPLEMENTATION (04/30/2018)]

Part 6: Part 6: Proximal Policy Gradients [ARTICLE (MAY)]

Any questions 👨‍💻

If you have any questions, feel free to ask me:

📧: hello@simoninithomas.com

Github: https://github.com/simoninithomas/Deep_reinforcement_learning_Course

🌐 : https://simoninithomas.github.io/Deep_reinforcement_learning_Course/

Twitter: @ThomasSimonini

Don't forget to follow me on twitter, github and Medium to be alerted of the new articles that I publish

How to help 🙌

3 ways:

  • Clap our articles a lot:Clapping in Medium means that you really like our articles. And the more claps we have, the more our article is shared
  • Share and speak about our articles: By sharing our articles you help us to spread the word.
  • Improve our notebooks: if you found a bug or a better implementation you can send a pull request.