Analytical Insights in RL

Sergey discusses the relationship between time steps and problem characterization in reinforcement learning, emphasizing the importance of empirical selection for rollout lengths. He highlights that while model-based RL can enhance policy improvement, its effectiveness heavily relies on the accuracy of the model, which can vary significantly depending on the complexity of the observations.