Constructing Skill Trees for Reinforcement Learning Agents from Demonstration Trajectories

Part of Advances in Neural Information Processing Systems 23 (NIPS 2010)

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Authors

George Konidaris, Scott Kuindersma, Roderic Grupen, Andrew Barto

Abstract

<p>We introduce CST, an algorithm for constructing skill trees from demonstration trajectories in continuous reinforcement learning domains. CST uses a changepoint detection method to segment each trajectory into a skill chain by detecting a change of appropriate abstraction, or that a segment is too complex to model as a single skill. The skill chains from each trajectory are then merged to form a skill tree. We demonstrate that CST constructs an appropriate skill tree that can be further refined through learning in a challenging continuous domain, and that it can be used to segment demonstration trajectories on a mobile manipulator into chains of skills where each skill is assigned an appropriate abstraction.</p>