Regularizing Adversarial Imitation Learning Using Causal Invariance

TL;DR

This paper introduces a causal invariance regularization technique for adversarial imitation learning to reduce reliance on spurious correlations, improving policy robustness in complex environments.

Contribution

It proposes a novel regularization method based on causal invariance that can be integrated into existing adversarial imitation learning frameworks.

Findings

Regularization improves policy robustness against spurious correlations.

Effective in high-dimensional robot locomotion tasks.

Demonstrated success in both simple and complex environments.

Abstract

Imitation learning methods are used to infer a policy in a Markov decision process from a dataset of expert demonstrations by minimizing a divergence measure between the empirical state occupancy measures of the expert and the policy. The guiding signal to the policy is provided by the discriminator used as part of an versarial optimization procedure. We observe that this model is prone to absorbing spurious correlations present in the expert data. To alleviate this issue, we propose to use causal invariance as a regularization principle for adversarial training of these models. The regularization objective is applicable in a straightforward manner to existing adversarial imitation frameworks. We demonstrate the efficacy of the regularized formulation in an illustrative two-dimensional setting as well as a number of high-dimensional robot locomotion benchmark tasks.