Combined Model for Partially-Observable and Non-Observable Task
  Switching: Solving Hierarchical Reinforcement Learning Problems Statically
  and Dynamically with Transfer Learning
  An integral function of fully autonomous robots and humans is the ability to
focus attention on a few relevant percepts to reach a certain goal while
disregarding irrelevant percepts. Humans and animals rely on the interactions
between the Pre-Frontal Cortex (PFC) and the Basal Ganglia (BG) to achieve this
focus called Working Memory (WM). The Working Memory Toolkit (WMtk) was
developed based on a computational neuroscience model of this phenomenon with
Temporal Difference (TD) Learning for autonomous systems. Recent adaptations of
the toolkit either utilize Abstract Task Representations (ATRs) to solve
Non-Observable (NO) tasks or storage of past input features to solve
Partially-Observable (PO) tasks, but not both. We propose a new model,
PONOWMtk, which combines both approaches, ATRs and input storage, with a static
or dynamic number of ATRs. The results of our experiments show that PONOWMtk
performs effectively for tasks that exhibit PO, NO, or both properties.
