  Options represent a framework for reasoning across multiple time scales in
reinforcement learning (RL). With the recent active interest in the
unsupervised learning paradigm in the RL research community, the option
framework was adapted to utilize the concept of empowerment, which corresponds
to the amount of influence the agent has on the environment and its ability to
perceive this influence, and which can be optimized without any supervision
provided by the environment's reward structure. Many recent papers modify this
concept in various ways achieving commendable results. Through these various
modifications, however, the initial context of empowerment is often lost. In
this work we offer a comparative study of such papers through the lens of the
original empowerment principle.
