Isolating and Leveraging Controllable and Noncontrollable Visual Dynamics in World Models. (arXiv:2205.13817v1 [cs.LG])

World models learn the consequences of actions in vision-based interactive
systems. However, in practical scenarios such as autonomous driving, there
commonly exists noncontrollable dynamics independent of the action signals,
making it difficult to learn effective world models. To tackle this problem, we
present a novel reinforcement learning approach named Iso-Dream, which improves
the Dream-to-Control framework in two aspects. First, by optimizing the inverse
dynamics, we encourage the world model to learn controllable and
noncontrollable sources of spatiotemporal changes on isolated …

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