Chelsea discusses the growing interest in model-based reinforcement learning, particularly in vision-based domains. She highlights innovative approaches that predict future images using neural networks, which can enhance sample efficiency and improve policies in environments like Atari games. This progress challenges the dominance of model-free methods, showcasing the potential of model-based techniques in complex scenarios.
