Date Added: Sep 2011
Reinforcement Learning is an area of machine learning that studies the problem of solving sequential decision making problems. The agent must learn behavior through trial-and-error interaction with a dynamic environment. Learning efficiently in large scale problems and complex tasks demands a decomposition of the original complex task into simple and smaller subtasks. In this paper, the authors present a subgoal-based method for automatically creating useful skills in reinforcement Learning. Their method identifies subgoals using a local graph clustering algorithm.