Recent work in reinforcement learning has focused on several characteristics
of learned policies that go beyond maximizing reward. These properties include
fairness, explainability, generalization, and robustness. In this paper, we
define interventional robustness (IR), a measure of how much variability is
introduced into learned policies by incidental aspects of the training
procedure, such as the order of training data or the particular exploratory
actions taken by agents. A training procedure has high IR when the agents it
produces take very similar actions under intervention, despite variation in
these incidental aspects of the training procedure. We develop an intuitive,
quantitative measure of IR and calculate it for eight algorithms in three Atari
environments across dozens of interventions and states. From these experiments,
we find that IR varies with the amount of training and type of algorithm and
that high performance does not imply high IR, as one might expect.

本文提出了干预鲁棒性的概念，并开发了一种可量化的方法来测量它。通过对八个算法和三种 Atari 环境下的干预和状态进行计算，发现干预鲁棒性因算法类型和训练次数而异，高性能不一定意味着高干预鲁棒性。