Adversarial imitation learning has become a popular framework for imitation
in continuous control. Over the years, several variations of its components
were proposed to enhance the performance of the learned policies as well as the
sample complexity of the algorithm. In practice, these choices are rarely
tested all together in rigorous empirical studies. It is therefore difficult to
discuss and understand what choices, among the high-level algorithmic options
as well as low-level implementation details, matter. To tackle this issue, we
implement more than 50 of these choices in a generic adversarial imitation
learning framework and investigate their impacts in a large-scale study (>500k
trained agents) with both synthetic and human-generated demonstrations. While
many of our findings confirm common practices, some of them are surprising or
even contradict prior work. In particular, our results suggest that artificial
demonstrations are not a good proxy for human data and that the very common
practice of evaluating imitation algorithms only with synthetic demonstrations
may lead to algorithms which perform poorly in the more realistic scenarios
with human demonstrations.

该研究在大规模实验中研究了逾 50 个选择项，探讨其在区分高级算法选项和低级实现细节方面的影响，发现了一些惊人的结果，特别是以人类演示为基础的模拟场景可能会使模仿算法表现不佳。

对于敌对性模仿学习，什么是重要的？

What Matters for Adversarial Imitation Learning?

The rise of algorithmic decision-making has spawned much research on fair
machine learning (ML). Financial institutions use ML for building risk
scorecards that support a range of credit-related decisions. Yet, the
literature on fair ML in credit scoring is scarce. The paper makes three
contributions. First, we revisit statistical fairness criteria and examine
their adequacy for credit scoring. Second, we catalog algorithmic options for
incorporating fairness goals in the ML model development pipeline. Last, we
empirically compare different fairness processors in a profit-oriented credit
scoring context using real-world data. The empirical results substantiate the
evaluation of fairness measures, identify suitable options to implement fair
credit scoring, and clarify the profit-fairness trade-off in lending decisions.
We find that multiple fairness criteria can be approximately satisfied at once
and recommend separation as a proper criterion for measuring the fairness of a
scorecard. We also find fair in-processors to deliver a good balance between
profit and fairness and show that algorithmic discrimination can be reduced to
a reasonable level at a relatively low cost. The codes corresponding to the
paper are available on GitHub.

本研究探讨公平机器学习在征信评分中的应用，介绍了统计公平性准则、机器学习模型中公平性目标的算法选择，使用实际数据实证比较了不同的公平性处理器，发现多种公平性准则可以同时得到满足，提出分离度作为评估公平性的准则，证明了公平性处理器能在利润与公平性中找到一个良好的折中点，可以以较低的成本将算法歧视降低到合理的水平。