Exploring Computational User Models for Agent Policy Summarization

1 code implementation • 30 May 2019 • Isaac Lage, Daphna Lifschitz, Finale Doshi-Velez, Ofra Amir

We introduce an imitation learning-based approach to policy summarization; we demonstrate through computational simulations that a mismatch between the model used to extract a summary and the model used to reconstruct the policy results in worse reconstruction quality; and we demonstrate through a human-subject study that people use different models to reconstruct policies in different contexts, and that matching the summary extraction model to these can improve performance.

Imitation Learning