Learning Options for an MDP from Demonstrations

  • Marco Tamassia
  • Fabio Zambetta
  • William Raffe
  • Xiaodong Li
Conference paper

DOI: 10.1007/978-3-319-14803-8_18

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8955)
Cite this paper as:
Tamassia M., Zambetta F., Raffe W., Li X. (2015) Learning Options for an MDP from Demonstrations. In: Chalup S.K., Blair A.D., Randall M. (eds) Artificial Life and Computational Intelligence. ACALCI 2015. Lecture Notes in Computer Science, vol 8955. Springer, Cham

Abstract

The options framework provides a foundation to use hierarchical actions in reinforcement learning. An agent using options, along with primitive actions, at any point in time can decide to perform a macro-action made out of many primitive actions rather than a primitive action. Such macro-actions can be hand-crafted or learned. There has been previous work on learning them by exploring the environment. Here we take a different perspective and present an approach to learn options from a set of experts demonstrations. Empirical results are also presented in a similar setting to the one used in other works in this area.

Keywords

reinforcement learning options 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Marco Tamassia
    • 1
  • Fabio Zambetta
    • 1
  • William Raffe
    • 1
  • Xiaodong Li
    • 1
  1. 1.RMIT UniversityMelbourneAustralia

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