Book Content
chapters • 25h20m total length
1. Fundamentals of Reinforcement Learning
2. A Guide to the Gym Toolkit
3. The Bellman Equation and Dynamic Programming
4. Monte Carlo Methods
5. Understanding Temporal Difference Learning
6. Case Study – The MAB Problem
7. Deep Learning Foundations
8. A Primer on TensorFlow
9. Deep Q Network and Its Variants
10. Policy Gradient Method
11. Actor-Critic Methods – A2C and A3C
12. Learning DDPG, TD3, and SAC
13. TRPO, PPO, and ACKTR Methods
14. Distributional Reinforcement Learning
15. Imitation Learning and Inverse RL
16. Deep Reinforcement Learning with Stable Baselines
17. Reinforcement Learning Frontiers
18. Appendix 1 – Reinforcement Learning Algorithms
19. Appendix 2 – Assessments
