Book Content
chapters • 27h32m total length
1. What Is Reinforcement Learning?
2. OpenAI Gym
3. Deep Learning with PyTorch
4. The Cross-Entropy Method
5. Tabular Learning and the Bellman Equation
6. Deep Q-Networks
7. Higher-Level RL libraries
8. DQN Extensions
9. Ways to Speed up RL
10. Stocks Trading Using RL
11. Policy Gradients – an Alternative
12. The Actor-Critic Method
13. Asynchronous Advantage Actor-Critic
14. Training Chatbots with RL
15. The TextWorld environment
16. Web Navigation
17. Continuous Action Space
18. RL in Robotics
19. Trust Regions – PPO, TRPO, ACKTR, and SAC
20. Black-Box Optimization in RL
21. Advanced exploration
22. Beyond Model-Free – Imagination
23. AlphaGo Zero
24. RL in Discrete Optimisation
25. Multi-agent RL
