Provably Efficient Third-Person Imitation from Offline Observation
Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 124:1228-1237, 2020.
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.