Action Categorization for Computationally Improved Task Learning and
  Planning
  This paper explores the problem of task learning and planning, contributing
the Action-Category Representation (ACR) to improve computational performance
of both Planning and Reinforcement Learning (RL). ACR is an algorithm-agnostic,
abstract data representation that maps objects to action categories (groups of
actions), inspired by the psychological concept of action codes. We validate
our approach in StarCraft and Lightworld domains; our results demonstrate
several benefits of ACR relating to improved computational performance of
planning and RL, by reducing the action space for the agent.
