Learned Models in RL

Sergey discusses the nuances between learned models and physics-based models in reinforcement learning. He emphasizes the importance of data efficiency over parameter count, suggesting that having more parameters often leads to better performance. The conversation also touches on the advantages of incorporating physics knowledge when available, as it can enhance model accuracy and reduce error.