/Deep_reinforcement_learning_Course

Implementations from the free course Deep Reinforcement Learning with Tensorflow and PyTorch

Primary LanguageJupyter Notebook

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

Deep Reinforcement Course with Tensorflow and PyTorch

Chapter 1: Introduction to Deeep Reinforcement Learning

Chapter 2: Q-learning with Taxi-v3 🚕

📹 [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📅

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:

📧: 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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