Deep Reinforcement Learning Course

⚠️ The new version of Deep Reinforcement Learning Course starts on October the 2nd 2020. ➡️ More info here ⬅️

Syllabus

Chapter 1: Introduction to Deeep Reinforcement Learning

�� ARTICLE Introduction to Deep Reinforcement Learning

�� VIDEO Introduction to Deep Reinforcement Learning

Chapter 2: Q-learning with Taxi-v3 ��

�� ARTICLE: Q-Learning, let’s create an autonomous Taxi �� (Part 1/2)

VIDEO Q-Learning, let’s create an autonomous Taxi �� (Part 1/2)

�� [ARTICLE: Q-Learning, let’s create an autonomous Taxi �� (Part 2/2)] �� Friday ��

�� [VIDEO: Q-Learning, let’s create an autonomous Taxi �� (Part 2/2)] �� Friday ��

FROZENLAKE IMPLEMENTATION

�� Implementing a Q-learning agent that plays Taxi-v2 ��

Part 3: Deep Q-learning with Doom

�� ARTICLE // DOOM IMPLEMENTATION

�� Create a DQN Agent that learns to play Atari Space Invaders ��

Part 4: Policy Gradients with Doom

�� ARTICLE // CARTPOLE IMPLEMENTATION // DOOM IMPLEMENTATION

�� Create an Agent that learns to play Doom deathmatch

Part 3+: Improvments in Deep Q-Learning

�� ARTICLE// Doom Deadly corridor IMPLEMENTATION

�� Create an Agent that learns to play Doom Deadly corridor

Part 5: Advantage Advantage Actor Critic (A2C)

�� ARTICLE

�� Create an Agent that learns to play Sonic

Part 6: Proximal Policy Gradients

�� ARTICLE

��‍�� Create an Agent that learns to play Sonic the Hedgehog 2 and 3

Part 7: Curiosity Driven Learning made easy Part I

�� ARTICLE

Part 8: Random Network Distillation with PyTorch

��‍�� A trained RND agent that learned to play Montezuma's revenge (21 hours of training with a Tesla K80

Any questions ��‍��

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

�� : simonini.thomas.pro@gmail.com

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

�� : https://simoninithomas.github.io/deep-rl-course/

Twitter: @ThomasSimonini

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How to help ��

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