Book Content
chapters • 18h8m total length
1. Introduction to Reinforcement Learning
2. Multi-armed Bandits
3. Contextual Bandits
4. Makings of the Markov Decision Process
5. Solving the Reinforcement Learning Problem
6. Deep Q-Learning at Scale
7. Policy Based Methods
8. Model-Based Methods
9. Multi-Agent Reinforcement Learning
10. Machine Teaching
11. Generalization and Domain Randomization
12. Meta-reinforcement learning
13. Other Advanced Topics
14. Autonomous Systems
15. Supply Chain Management
16. Marketing, Personalization and Finance
17. Smart City and Cybersecurity
18. Challenges and Future Directions in Reinforcement Learning
