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Deep Reinforcement Learning Course

โš ๏ธ I'm currently updating the implementations (December (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 ๐Ÿ†• about Deep Reinforcement Learning, where we'll learn the main algorithms, and how to implement them with Tensorflow.

๐Ÿ“œThe articles explain the concept from the big picture to the mathematical details behind it.

๐Ÿ“น The videos explain how to create the agent with Tensorflow

Syllabus

๐Ÿ“œ Part 1: Introduction to Reinforcement Learning ARTICLE

Part 2: Q-learning with FrozenLake

๐Ÿ“œ ARTICLE // FROZENLAKE IMPLEMENTATION

๐Ÿ“น Implementing a Q-learning agent that plays Taxi-v2 ๐Ÿš•

Part 3: Deep Q-learning with Doom

๐Ÿ“œ ARTICLE // DOOM IMPLEMENTATION

๐Ÿ“น Create a DQN Agent that learns to play Atari Space Invaders ๐Ÿ‘พ

Part 4: Policy Gradients with Doom

๐Ÿ“œ ARTICLE // CARTPOLE IMPLEMENTATION // DOOM IMPLEMENTATION

๐Ÿ“น Create an Agent that learns to play Doom deathmatch

Part 3+: Improvments in Deep Q-Learning

๐Ÿ“œ ARTICLE// Doom Deadly corridor IMPLEMENTATION

๐Ÿ“น Create an Agent that learns to play Doom Deadly corridor

Part 5: Advantage Advantage Actor Critic (A2C)

๐Ÿ“œ ARTICLE

๐Ÿ“น Create an Agent that learns to play Sonic

Part 6: Proximal Policy Gradients

๐Ÿ“œ ARTICLE

๐Ÿ‘จโ€๐Ÿ’ป Create an Agent that learns to play Sonic the Hedgehog 2 and 3

Part 7: Curiosity Driven Learning made easy Part I

๐Ÿ“œ ARTICLE

Any questions ๐Ÿ‘จโ€๐Ÿ’ป

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

๐Ÿ“ง: hello@simoninithomas.com

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

๐ŸŒ : 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 ๐Ÿ™Œ

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.