AI RESEARCH
Adversarial Imitation Learning with General Function Approximation: Theoretical Analysis and Practical Algorithms
arXiv CS.LG
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ArXi:2605.01778v1 Announce Type: new Adversarial imitation learning (AIL), a prominent approach in imitation learning, has achieved significant practical success powered by neural network approximation. However, existing theoretical analyses of AIL are primarily confined to simplified settings, such as tabular and linear function approximation, and involve complex algorithmic designs that impede practical implementation. This creates a substantial gap between theory and practice.