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ABC: Adversarial Behavioral Cloning for Offline Mode-Seeking Imitation Learning. (arXiv:2211.04005v1 [cs.LG])
Nov. 9, 2022, 2:11 a.m. | Eddy Hudson, Ishan Durugkar, Garrett Warnell, Peter Stone
cs.LG updates on arXiv.org arxiv.org
Given a dataset of expert agent interactions with an environment of interest,
a viable method to extract an effective agent policy is to estimate the maximum
likelihood policy indicated by this data. This approach is commonly referred to
as behavioral cloning (BC). In this work, we describe a key disadvantage of BC
that arises due to the maximum likelihood objective function; namely that BC is
mean-seeking with respect to the state-conditional expert action distribution
when the learner's policy is represented …
More from arxiv.org / cs.LG updates on arXiv.org
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