Subgoal Discovery in Reinforcement Learning Using Local Graph Clustering
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.