Explaining predictions based on multivariate time series data carries the
additional difficulty of handling not only multiple features, but also time
dependencies. It matters not only what happened, but also when, and the same
feature could have a very different impact on a prediction depending on this
time information. Previous work has used perturbation-based saliency methods to
tackle this issue, perturbing an input using a trainable mask to discover which
features at which times are driving the predictions. However these methods
introduce fixed perturbations, inspired from similar methods on static data,
while there seems to be little motivation to do so on temporal data. In this
work, we aim to explain predictions by learning not only masks, but also
associated perturbations. We empirically show that learning these perturbations
significantly improves the quality of these explanations on time series data.

通过学习掩码和相关扰动，来解释基于多元时间序列数据的预测，显著提高了对时间序列数据的解释质量。

学习扰动来解释时间序列预测

Learning Perturbations to Explain Time Series Predictions

Since the publication of the original Transformer architecture (Vaswani et
al. 2017), Transformers revolutionized the field of Natural Language
Processing. This, mainly due to their ability to understand timely dependencies
better than competing RNN-based architectures. Surprisingly, this architecture
change does not affect the field of Reinforcement Learning (RL), even though
RNNs are quite popular in RL, and time dependencies are very common in RL.
Recently, Parisotto et al. 2019) conducted the first promising research of
Transformers in RL. To support the findings of this work, this paper seeks to
provide an additional example of a Transformer-based RL method. Specifically,
the goal is a simple Transformer-based Deep Q-Learning method that is stable
over several environments. Due to the unstable nature of Transformers and RL,
an extensive method search was conducted to arrive at a final method that
leverages developments around Transformers as well as Q-learning. The proposed
method can match the performance of classic Q-learning on control environments
while showing potential on some selected Atari benchmarks. Furthermore, it was
critically evaluated to give additional insights into the relation between
Transformers and RL.

本文提出了一种基于 Transformer 的 Deep Q-Learning 方法，该方法在稳定性和 Atari 基准方面表现良好，并为探究 Transformer 与强化学习之间的关系提供了额外见解。