Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models

In this episode, we discuss Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models by Vlad Sobal, Wancong Zhang, Kyunghyun Cho, Randall Balestriero, Tim G. J. Rudner, Yann LeCun. The paper compares model-free reinforcement learning and model-based control methods for solving navigation tasks using offline, reward-free data. It finds that reinforcement learning performs best with large, high-quality datasets, while model-based planning with latent dynamics models generalizes better to new environments and handles suboptimal data more efficiently. Overall, latent model-based planning is highlighted as a robust approach for offline learning and adapting to diverse tasks.