/Deep_reinforcement_learning_Course

Implementations from the free course Deep Reinforcement Learning with Tensorflow

Primary LanguageJupyter Notebook

⚠️ I'm currently updating the implementations (January and February (some delay due to job interviews)) with Tensorflow and PyTorch.

Deep Reinforcement Course with Tensorflow

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

📜The articles explain the concept from the big picture to the mathematical details behind it.

📹 The videos explain how to create the agent with Tensorflow

📜 Part 1: Introduction to Reinforcement Learning ARTICLE

Part 2: Q-learning with FrozenLake

Part 3: Deep Q-learning with Doom

Part 4: Policy Gradients with Doom

Part 3+: Improvments in Deep Q-Learning

Part 5: Advantage Advantage Actor Critic (A2C)

📜 ARTICLE

Part 6: Proximal Policy Gradients

📜 ARTICLE

Part 7: Curiosity Driven Learning made easy Part I

📜 ARTICLE

Part 8: Random Network Distillation with PyTorch

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 and like our videos a lot:Clapping in Medium means that you really like our articles. And the more claps we have, the more our article is shared Liking our videos help them to be much more visible to the deep learning community.
  • Share and speak about our articles and videos: By sharing our articles and videos 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.