Provably Efficient Third-Person Imitation from Offline Observation

2020-02-27 · Aaron Zweig, Joan Bruna

Domain adaptation in imitation learning represents an essential step towards improving generalizability. However, even in the restricted setting of third-person imitation where transfer is between isomorphic Markov Decision Processes, there are no strong guarantees on the performance of transferred policies. We present problem-dependent, statistical learning guarantees for third-person imitation from observation in an offline setting, and a lower bound on performance in the online setting.

📄 PDF Abstract BibTeX arXiv:2002.12446

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