DIGIC: Domain Generalizable Imitation Learning by Causal Discovery
CoRR(2024)
摘要
Causality has been combined with machine learning to produce robust
representations for domain generalization. Most existing methods of this type
require massive data from multiple domains to identify causal features by
cross-domain variations, which can be expensive or even infeasible and may lead
to misidentification in some cases. In this work, we make a different attempt
by leveraging the demonstration data distribution to discover the causal
features for a domain generalizable policy. We design a novel framework, called
DIGIC, to identify the causal features by finding the direct cause of the
expert action from the demonstration data distribution via causal discovery.
Our framework can achieve domain generalizable imitation learning with only
single-domain data and serve as a complement for cross-domain variation-based
methods under non-structural assumptions on the underlying causal models. Our
empirical study in various control tasks shows that the proposed framework
evidently improves the domain generalization performance and has comparable
performance to the expert in the original domain simultaneously.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要