moshebeutel / deep_reinforcement_learning_course

Notebooks from our series of blogpost about Deep Reinforcement Learning

Deep Reinforcement Learning Course

This notebook is part of the Free 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:

📧: [email protected]

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

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

Twitter: @ThomasSimonini

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• Improve our notebooks: if you found a bug or a better implementation you can send a pull request.