We address the problem of imitation learning with multi-modal demonstrations. Instead of attempting to learn all modes, we argue that in many tasks it is sufficient to imitate any one of them. We show that the state-of-the-art methods such as GAIL and behavior cloning, due to their choice of loss function, often incorrectly interpolate between such modes. Our key insight is to minimize the right divergence between the learner and the expert state-action distributions, namely the reverse KL divergence or I-projection. We propose a general imitation learning framework for estimating and minimizing any f-Divergence. By plugging in different divergences, we are able to recover existing algorithms such as Behavior Cloning (Kullback-Leibler), GAIL (Jensen Shannon) and Dagger (Total Variation). Empirical results show that our approximate I-projection technique is able to imitate multi-modal behaviors more reliably than GAIL and behavior cloning.

本文提出了一种使用多模演示的模仿学习方法，针对现有方法中插值错误的问题，采用与专家状态-行动分布的正向KL散度相对应的反向KL散度，即I-projection，作为不同f-散度估计和最小化的框架，并得出了比GAIL和行为克隆更加可靠的多模行为近似I-projection方法。

基于f-差距最小化的模仿学习