Deep Reinforcement Learning Course in Pytorch

Unofficial Pytroch (1.7+) implementation of the original Deep Reinforcement Learning Course

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 on theory and Tensorflow implementation, please contact the original author:

📧: simonini.thomas.pro@gmail.com

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

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