  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 state transition
branches. Second, we optimize the behavior of the agent on the decoupled latent
imaginations of the world model. Specifically, to estimate state values, we
roll-out the noncontrollable states into the future and associate them with the
current controllable state. In this way, the isolation of dynamics sources can
greatly benefit long-horizon decision-making of the agent, such as a
self-driving car that can avoid potential risks by anticipating the movement of
other vehicles. Experiments show that Iso-Dream is effective in decoupling the
mixed dynamics and remarkably outperforms existing approaches in a wide range
of visual control and prediction domains.
