Adversarial Imitation Learning with General Function Approximation: Theoretical Analysis and Practical Algorithms
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. This paper bridges this gap by exploring the theoretical underpinnings of online AIL with general function approximation. We introduce a novel framework called optimization-based AIL (OPT-AIL), which performs online optimization for reward learning ...
| Authors | Tian Xu, Zhilong Zhang, Zexuan Chen et al. |
|---|---|
| Primary category | cs.LG |
| Published | 3 May 2026 |
| Last updated at source | 3 May 2026 |
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