DIGIC: Domain Generalizable Imitation Learning by Causal Discovery
Yang Chen Yitao Liang Zhouchen Lin
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.