Explore model-based reinforcement learning in this comprehensive lecture, covering key concepts such as introduction, examples, pseudocode, complex models, and comparisons with replay buffers. Delve into practical applications of Dyna, search tree techniques, planning strategies, and Monte Carlo methods. Gain a deep understanding of how model-based RL differs from other approaches and learn to implement these concepts in real-world scenarios.