Book Content
chapters • 12h12m total length
1. The Landscape of Reinforcement Learning
2. Implementing RL Cycle and OpenAI Gym
3. Solving Problems with Dynamic Programming
4. Q learning and SARSA Applications
5. Deep Q-Network
6. Learning Stochastic and DDPG optimization
7. TRPO and PPO implementation
8. DDPG and TD3 Applications
9. Model-Based RL
10. Imitation Learning with the DAgger Algorithm
11. Understanding Black-Box Optimization Algorithms
12. Developing the ESBAS Algorithm
13. Practical Implementation for Resolving RL Challenges
