OPT-AIL provides the first provably efficient adversarial imitation learning algorithms under general function approximation, achieving polynomial expert sample and interaction complexity.
Is behavior cloning all you need? understanding horizon in imitation learning
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MA-BC partitions divergent expert data and pools non-conflicting pairs to achieve faster convergence to Pareto-optimal policies in MOMDPs, with a matching minimax lower bound.
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Adversarial Imitation Learning with General Function Approximation: Theoretical Analysis and Practical Algorithms
OPT-AIL provides the first provably efficient adversarial imitation learning algorithms under general function approximation, achieving polynomial expert sample and interaction complexity.
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Split the Differences, Pool the Rest: Provably Efficient Multi-Objective Imitation
MA-BC partitions divergent expert data and pools non-conflicting pairs to achieve faster convergence to Pareto-optimal policies in MOMDPs, with a matching minimax lower bound.