Wancong Zhang

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

A long-standing goal in AI is to develop agents capable of solving diverse tasks across a range of environments, including those never seen during training. Two dominant paradigms address this challenge: (i) reinforcement learning (RL), which learns policies via trial and error, and (ii) optimal con…