We provide a unifying view of a large family of previous imitation learning algorithms through the lens of moment matching. At its core, our classification scheme is based on whether the learner attempts to match (1) reward or (2) action-value moments of the expert's behavior, with each option leading to differing algorithmic approaches. By considering adversarially chosen divergences between learner and expert behavior, we are able to derive bounds on policy performance that apply for all algorithms in each of these classes, the first to our knowledge. We also introduce the notion of recoverability, implicit in many previous analyses of imitation learning, which allows us to cleanly delineate how well each algorithmic family is able to mitigate compounding errors. We derive two novel algorithm templates, AdVIL and AdRIL, with strong guarantees, simple implementation, and competitive empirical performance.

中文翻译：

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时刻与匹配：模仿学习中的权衡与处理

我们通过矩匹配的角度提供了一大类先前的模仿学习算法的统一视图。归根结底，我们的分类方案基于学习者是否尝试匹配专家行为的（1）奖励或（2）行动价值时刻，每种选择都导致不同的算法方法。通过考虑学习者行为与专家行为之间的对抗性选择差异，我们能够得出政策绩效的界限，该界限适用于所有这些类别中的所有算法，这是我们所了解的第一个。我们还介绍了可恢复性的概念，该概念在以前的许多模仿学习分析中都没有体现，这使我们能够清楚地描述每个算法系列能够缓解复合错误的程度。我们推导了两个新颖的算法模板AdVIL和AdRIL，