MOCI jointly infers shared constraints and individual preferences from heterogeneous expert trajectories via multi-objective inverse reinforcement learning and outperforms baselines on grid-world predictive performance.
Your learned constraint is secretly a backward reachable tube.Reinforcement Learning Journal, 6: 478–492
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Multi-Objective Constraint Inference using Inverse reinforcement learning
MOCI jointly infers shared constraints and individual preferences from heterogeneous expert trajectories via multi-objective inverse reinforcement learning and outperforms baselines on grid-world predictive performance.