  World models learn the consequences of actions in vision-based interactive
systems. However, in practical scenarios like autonomous driving,
noncontrollable dynamics that are independent or sparsely dependent on action
signals often exist, making it challenging to learn effective world models. To
address this issue, we propose Iso-Dream++, a model-based reinforcement
learning approach that has two main contributions. First, we optimize the
inverse dynamics to encourage the world model to isolate controllable state
transitions from the mixed spatiotemporal variations of the environment.
Second, we perform policy optimization based on the decoupled latent
imaginations, where we roll out noncontrollable states into the future and
adaptively associate them with the current controllable state. This enables
long-horizon visuomotor control tasks to benefit from isolating mixed dynamics
sources in the wild, such as self-driving cars that can anticipate the movement
of other vehicles, thereby avoiding potential risks. On top of our previous
work, we further consider the sparse dependencies between controllable and
noncontrollable states, address the training collapse problem of state
decoupling, and validate our approach in transfer learning setups. Our
empirical study demonstrates that Iso-Dream++ outperforms existing
reinforcement learning models significantly on CARLA and DeepMind Control.
