Corpus ID: 210921270

Interpretable Apprenticeship Learning from Heterogeneous Decision-Making via Personalized Embeddings.

@article{Paleja2020InterpretableAL,
  title={Interpretable Apprenticeship Learning from Heterogeneous Decision-Making via Personalized Embeddings.},
  author={Rohan R. Paleja and A. H. T. Eranga De Silva and Letian Chen and Matthew Craig Gombolay},
  journal={arXiv: Learning},
  year={2020}
}
  • Rohan R. Paleja, A. H. T. Eranga De Silva, +1 author Matthew Craig Gombolay
  • Published 2020
  • Computer Science, Mathematics
  • arXiv: Learning
  • Advances in learning from demonstration (LfD) have enabled intelligent agents to learn decision-making strategies through observation. However, humans exhibit heterogeneity in their decision-making criteria, leading to demonstrations with significant variability. We propose a personalized apprenticeship learning framework that automatically infers an interpretable representation of all human task demonstrators by extracting latent, human-specific decision-making criteria specified by an… CONTINUE READING

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