Lessons

1. Introduction

2. Setting up your Reinforcement Learning Environment

3. Markov Decision Processes

4. Introduction to the OpenAI Gym Interface

5. $Q$ learning

6. Gym Wrappers

7. Function Approximation and Tensorflow

8. $Q$-learning with Tensorflow

9. Deep $Q$-learning

10. Rainbow - Improvements to Deep $Q$-learning

11. Policy Gradients

12. Advantage Actor-Critic (A2C)

13. Generalized Advantage Estimation (GAE)

14. Trust Region Policy Optimization (TRPO)

15. Proximal Policy Optimization (PPO)

16. Entropy

17. KL-Divergence

18. List of Important Papers

19. Neural Network Design