Constructing skill trees for reinforcement learning agents from demonstration trajectories

  • Konidaris G
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Abstract

We introduce CST, an algorithm for constructing skill trees from demonstration trajectories in continuous reinforcement learning domains. CST uses a change- point 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.

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  • SGR: 84860608588
  • PUI: 364743746
  • SCOPUS: 2-s2.0-84860608588
  • ISBN: 9781617823800

Authors

  • George Konidaris

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