Chelsea discusses the implications of not fully training agents to convergence, highlighting how early stopping can impact performance comparisons. She emphasizes the challenges of using logged data for experimental setups in reinforcement learning, particularly in real-world applications. The conversation also delves into the concept of distribution mismatch, illustrating its significance through visualizations that clarify the differences in states and actions between policies.
