Original article was published by Thomas Simonini on Artificial Intelligence on Medium
🎉 I’m happy to announce the launch of the new version of the Deep Reinforcement Learning Course 🥳, a free course from beginner to expert where you learn to master the skills and architectures you need, to become a deep reinforcement learning expert with Tensorflow and PyTorch.
Since the launch of the first version in 2018, we had more than 40,000 claps, 2,500 GitHub stars.
Since then, a lot of breakthroughs happened in Deep RL. New libraries were published and some of our implementations become obsolete. That’s why, in order to keep up the pace of the breakthrough, we publish this new version.
What this new version will look like?
The foundations will be composed of 10 chapters each about an architecture or a topic, each of them will be an article and a video.
You can check the syllabus on the Deep Reinforcement Learning Course’s website.
- Introduction to Reinforcement Learning
- Deep Q-Learning
- Improvements in Deep Q-Learning
- Policy Gradients Methods
- Actor Critic Methods (A3C, A2C)
- Proximal Policy Optimization (PPO)
- Soft-Actor Critic (SAC)
- Curiosity Driven Learning
- Curiosity through Random Network Distillation
As for the first version, for each chapter, we will explain deeply the topic and the mathematical details behind it.
And then, we’ll dive on a complete implementation of the agent with Tensorflow and PyTorch.