Split the Differences, Pool the Rest: Provably Efficient Multi-Objective Imitation
This work investigates multi-objective imitation learning: the problem of recovering policies that lie on the Pareto front given demonstrations from multiple Pareto-optimal experts in a Multi-Objective Markov Decision Process (MOMDP). Standard imitation approaches are ill-equipped for this regime, as naively aggregating conflicting expert trajectories can result in dominated policies. To address this, we introduce Multi-Output Augmented Behavioral Cloning (MA-BC), an algorithm that systematically partitions divergent expert data while pooling state-action pairs where no behavior conflict is observed. Theoretically, we prove that MA-BC converges to Pareto-optimal policies at a faster stati...
| Authors | Ziyad Sheebaelhamd, Luca Viano, Volkan Cevher et al. |
|---|---|
| Primary category | cs.LG |
| Published | 12 May 2026 |
| Last updated at source | 14 October 2026 |
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