Split the Differences, Pool the Rest: Provably Efficient Multi-Objective Imitation

This paper tackles multi‑objective imitation learning, aiming to recover policies on the Pareto front from demonstrations by multiple Pareto‑optimal experts in a Multi‑Objective Markov Decision Process. The authors propose Multi‑Output Augmented Behavioral Cloning (MA‑BC), which partitions divergent expert data and pools non‑conflicting state‑action pairs, proving that MA‑BC converges to Pareto‑optimal policies faster than independent‑dataset learners and is minimax optimal. Experiments on diverse discrete environments and a continuous Linear Quadratic Regulator confirm the algorithm’s effectiveness.

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• reinforcement-learning