AI BriefingMA-BC: Provably Efficient Multi-Objective Imitation Learning via Selective Data Pooling
Key point
The new MA-BC method pools demonstrations only when expert actions agree, providing upper and lower bounds on sample complexity.
Details
Researchers introduce MA-BC, a new algorithm for multi-objective imitation learning that addresses the challenge of learning from experts with different objectives. Traditional methods either pool all data, which can obscure trade-offs, or learn from each expert separately, missing opportunities for data sharing.
How it works MA-BC selectively pools demonstrations only when the observed actions from different experts do not disagree. This approach preserves the distinct trade-offs of each expert while leveraging shared data where objectives align.
Key Contributions
- Provides upper and lower bounds on sample complexity, offering theoretical guarantees for efficiency.
- Authors: Ziyad Sheebaelhamd, Luca Viano, Volkan Cevher, Claire Vernade.
- Code available on GitHub and paper on arXiv.
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