Discovering Temporal Structure: An Overview of Hierarchical Reinforcement Learning

Understand

Developing agents capable of exploring, planning and learning in complex open-ended environments is a grand challenge in artificial intelligence (AI).

• Hierarchical reinforcement learning (HRL) offers a promising solution to this challenge by discovering and exploiting the temporal structure within a stream of experience.
• The strong appeal of the HRL framework has led to a rich and diverse body of literature attempting to discover a useful structure.
• However, it is still not clear how one might define what constitutes good structure in the first place, or the kind of problems in which identifying it may be helpful.