We evaluate the robustness of a Neural Architecture Search (NAS) algorithm
known as Efficient NAS (ENAS) against data agnostic poisoning attacks on the
original search space with carefully designed ineffective operations. We
empirically demonstrate how our one shot search space poisoning approach
exploits design flaws in the ENAS controller to degrade predictive performance
on classification tasks. With just two poisoning operations injected into the
search space, we inflate prediction error rates for child networks upto 90% on
the CIFAR-10 dataset.

本文研究了神经架构搜索算法 Efficient NAS 对数据毒化攻击的鲁棒性，通过注入无效操作到搜索空间中，我们展示了一次性搜索空间毒化方法如何利用 ENAS 控制器中的设计缺陷，降低分类任务中预测性能，只需向搜索空间注入两个毒化操作即可使得子网络在 CIFAR-10 数据集上的预测错误率增加高达 90%。

神经架构搜索中的单次搜索空间污染

Towards One Shot Search Space Poisoning in Neural Architecture Search

The objective of many real-world tasks is complex and difficult to
procedurally specify. This makes it necessary to use reward or imitation
learning algorithms to infer a reward or policy directly from human data.
Existing benchmarks for these algorithms focus on realism, testing in complex
environments. Unfortunately, these benchmarks are slow, unreliable and cannot
isolate failures. As a complementary approach, we develop a suite of simple
diagnostic tasks that test individual facets of algorithm performance in
isolation. We evaluate a range of common reward and imitation learning
algorithms on our tasks. Our results confirm that algorithm performance is
highly sensitive to implementation details. Moreover, in a case-study into a
popular preference-based reward learning implementation, we illustrate how the
suite can pinpoint design flaws and rapidly evaluate candidate solutions. The
environments are available at this https URL .

通过开发一套简单的诊断任务，并对常见的奖励学习和模仿学习算法进行评估，本研究证实算法的性能高度依赖于实现细节，其中还揭示了套件如何确定设计缺陷并快速评估解决方案的案例研究。

DERAIL：面向奖励和模仿学习的诊断环境

DERAIL: Diagnostic Environments for Reward And Imitation Learning

Games are often designed to shape player behavior in a desired way; however,
it can be unclear how design decisions affect the space of behaviors in a game.
Designers usually explore this space through human playtesting, which can be
time-consuming and of limited effectiveness in exhausting the space of possible
behaviors. In this paper, we propose the use of automated planning agents to
simulate humans of varying skill levels to generate game playthroughs. Metrics
can then be gathered from these playthroughs to evaluate the current game
design and identify its potential flaws. We demonstrate this technique in two
games: the popular word game Scrabble and a collectible card game of our own
design named Cardonomicon. Using these case studies, we show how using
simulated agents to model humans of varying skill levels allows us to extract
metrics to describe game balance (in the case of Scrabble) and highlight
potential design flaws (in the case of Cardonomicon).

本文提出使用自动规划代理来模拟不同技能水平的人来生成游戏过程，并从中收集指标以评估当前的游戏设计并确定其潜在缺陷。本文以 Scrabble 和 Cardonomicon 为案例，展示了使用模拟代理来模拟人类玩家如何从游戏中提取度量（在 Scrabble 的情况下）以及突出设计缺陷（在 Cardonomicon 的情况下）。