Book Content
chapters • 14h24m total length
1. Understanding Rewards-Based Learning
2. Dynamic Programming and the Bellman Equation
3. Monte Carlo Methods
4. Temporal Difference Learning
5. Exploring SARSA
6. Going Deep with DQN
7. Going Deeper with DDQN
8. Policy Gradient Methods
9. Optimizing for Continuous Control
10. All about Rainbow DQN
11. Exploiting ML-Agents
12. DRL Frameworks
13. 3D Worlds
14. From DRL to AGI














