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Co-Evolving Latent Action World Models
Yucen Wang, Fengming Zhang, De-Chuan Zhan +3
Adapting pretrained video generation models into controllable world models via latent actions is a promising step towards creating generalist world models. The dominant paradigm ad…
Reward Models in Deep Reinforcement Learning: A Survey
Rui Yu, Shenghua Wan, Yucen Wang +4
In reinforcement learning (RL), agents continually interact with the environment and use the feedback to refine their behavior. To guide policy optimization, reward models are intr…
AD3: Implicit Action is the Key for World Models to Distinguish the Diverse Visual Distractors
Yucen Wang, Shenghua Wan, Le Gan +2
Model-based methods have significantly contributed to distinguishing task-irrelevant distractors for visual control. However, prior research has primarily focused on heterogeneous…
SeMAIL: Eliminating Distractors in Visual Imitation via Separated Models
Shenghua Wan, Yucen Wang, Minghao Shao +2
Model-based imitation learning (MBIL) is a popular reinforcement learning method that improves sample efficiency on high-dimension input sources, such as images and videos. Followi…