Intrinsically motivated option learning: a comparative study of recent methods
This work addresses the need for clarity in unsupervised reinforcement learning research by evaluating recent advancements in option learning, but it is incremental as it primarily reviews and compares existing methods without introducing new techniques.
The paper tackles the problem of comparing recent methods for intrinsically motivated option learning in reinforcement learning, focusing on how modifications to the empowerment concept may lose its original context, and provides a comparative study to analyze these methods.
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.