Discovering Portable Options through Automated Mapping

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DesJardins_HPCF_Poster.pdf (327.54 KB)

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http://hpcf-files.umbc.edu/research/posters/DesJardins_HPCF_Poster.pdf

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Abstract

Goal for artificial agents: Learn the most efficient process for completing a task in a given domain o Corollary: Reuse and transfer learned knowledge o Previous work assumed that a mapping was provided or that all domains were identical o Our contributions:  Automatically map across domains with different objects and attributes  Leverage prior knowledge by identifying commonalities between source and target domains  Provide novel techniques for scoring mappings and abstracting domains o Our method outperforms Pickett and Barto's PolicyBlocks (2002) and MacGlashan's Transfer Options (2013)