  Model-based reinforcement learning (RL) algorithms designed for handling
complex visual observations typically learn some sort of latent state
representation, either explicitly or implicitly. Standard methods of this sort
do not distinguish between functionally relevant aspects of the state and
irrelevant distractors, instead aiming to represent all available information
equally. We propose a modified objective for model-based RL that, in
combination with mutual information maximization, allows us to learn
representations and dynamics for visual model-based RL without reconstruction
in a way that explicitly prioritizes functionally relevant factors. The key
principle behind our design is to integrate a term inspired by variational
empowerment into a state-space model based on mutual information. This term
prioritizes information that is correlated with action, thus ensuring that
functionally relevant factors are captured first. Furthermore, the same
empowerment term also promotes faster exploration during the RL process,
especially for sparse-reward tasks where the reward signal is insufficient to
drive exploration in the early stages of learning. We evaluate the approach on
a suite of vision-based robot control tasks with natural video backgrounds, and
show that the proposed prioritized information objective outperforms
state-of-the-art model based RL approaches with higher sample efficiency and
episodic returns. https://sites.google.com/view/information-empowerment
