在专家可观测和专家不可观测混杂下的因果模仿学习
Causal Imitation Learning under Expert-Observable and Expert-Unobservable Confounding
AI总结：
本文提出DML-IL算法，通过工具变量回归解决因果模仿学习中的隐藏混杂问题，并在连续状态-动作环境中优于现有基线方法。
摘要：
我们提出了一种通用的因果模仿学习（IL）框架，用于处理隐藏的混杂因素，该框架涵盖了几种现有的设置。我们的框架考虑了两种类型的隐藏混杂因素：（a）由专家观测但不被模仿者观测的变量，以及（b）从两者都隐藏的混杂噪声。通过利用轨迹历史作为工具变量，我们将框架中的因果IL重新公式化为条件矩限制（CMR）问题。我们提出了DML-IL算法，该算法通过工具变量回归解决此CMR问题，并上界其模仿差距。在连续状态-动作环境中，包括MuJoCo任务的实验证明，DML-IL在现有因果IL基线中表现更优。
英文摘要：
We propose a general framework for causal Imitation Learning (IL) with hidden confounders, which subsumes several existing settings. Our framework accounts for two types of hidden confounders: (a) variables observed by the expert but not by the imitator, and (b) confounding noise hidden from both. By leveraging trajectory histories as instruments, we reformulate causal IL in our framework into a Conditional Moment Restriction (CMR) problem. We propose DML-IL, an algorithm that solves this CMR problem via instrumental variable regression, and upper bound its imitation gap. Empirical evaluation on continuous state-action environments, including Mujoco tasks, demonstrates that DML-IL outperforms existing causal IL baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
