Split the Differences, Pool the Rest: Provably Efficient Multi-Objective Imitation [R]
Mirrored from r/MachineLearning for archival readability. Support the source by reading on the original site.
| | TLDR: The question we answer: how do you learn from experts with different objectives? Pooling all their data can lose their trade-offs; learning from each expert separately misses opportunities to share data. MA-BC pools demonstrations where observed actions don’t disagree, with upper and lower bounds on sample complexity. [link] [comments] |
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