Book Content
chapters • 27h24m total length
1. Introduction to Reinforcement Learning
2. Markov Decision Processes and Bellman Equations
3. Deep Learning in Practice with TensorFlow 2
4. Getting Started with OpenAI and TensorFlow for Reinforcement Learning
5. Dynamic Programming
6. Monte Carlo Methods
7. Temporal Difference Learning
8. The Multi-Armed Bandit Problem
9. What Is Deep Q Learning?
10. Playing an Atari Game with Deep Recurrent Q Networks
11. Policy-Based Methods for Reinforcement Learning
12. Evolutionary Strategies for RL
