Disentangling Dynamics and Content for Control and Planning

Understand

In this paper, We study the problem of learning a controllable representation for high-dimensional observations of dynamical systems.

• Specifically, we consider a situation where there are multiple sets of observations of dynamical systems with identical underlying dynamics.
• Only one of these sets has information about the effect of actions on the observation and the rest are just some random observations of the system.
• Our goal is to utilize the information in that one set and find a representation for the other sets that can be used for planning and ling-term prediction.