Object-Oriented Curriculum Generation for Reinforcement Learning

Felipe Leno da Silva, Anna Helena Reali Costa

Abstract: Autonomously learning a complex task takes a very long time for Reinforcement Learning (RL) agents. One way to learn faster is by dividing a complex task into several simple subtasks and organizing them into a Curriculum that guides Transfer Learning (TL) methods to reuse knowledge in a convenient sequence. However, previous works do not take into account the TL method to build specialized Curricula , leaving the burden of a careful subtask selection to a human. We here contribute novel procedures for: (i) dividing the target task into simpler ones under minimal human supervision; (ii) automatically generating Curricula based on object-oriented task descriptions; and (iii) using generated Curricula for reusing knowledge across tasks. Our experiments show that our proposal achieves a better performance using both manually given and generated subtasks when compared to the state-of-the-art technique in two different domains.