In this paper we combine one method for hierarchical reinforcement learning -
the options framework - with deep Q-networks (DQNs) through the use of
different "option heads" on the policy network, and a supervisory network for
choosing between the different options. We utilise our setup to investigate the
effects of architectural constraints in subtasks with positive and negative
transfer, across a range of network capacities. We empirically show that our
augmented DQN has lower sample complexity when simultaneously learning subtasks
with negative transfer, without degrading performance when learning subtasks
with positive transfer.

本文将一个分层强化学习方法 —— 选项框架与深度 Q 网络结合起来，通过在策略网络上使用不同的 “选项头” 以及用于选择不同选项的监督网络，探究子任务在正 / 负转移方面对架构约束的影响，并通过实证方法证明，增强型深度 Q 网络同时学习具有负转移的子任务的样本复杂性较低，而在学习具有正转移的子任务时不会降低性能。

深度强化学习的分类选项

Classifying Options for Deep Reinforcement Learning

Recent years have brought a significant growth in the volume of research in
sentiment analysis, mostly on highly subjective text types (movie or product
reviews). The main difference these texts have with news articles is that their
target is clearly defined and unique across the text. Following different
annotation efforts and the analysis of the issues encountered, we realised that
news opinion mining is different from that of other text types. We identified
three subtasks that need to be addressed: definition of the target; separation
of the good and bad news content from the good and bad sentiment expressed on
the target; and analysis of clearly marked opinion that is expressed
explicitly, not needing interpretation or the use of world knowledge.
Furthermore, we distinguish three different possible views on newspaper
articles - author, reader and text, which have to be addressed differently at
the time of analysing sentiment. Given these definitions, we present work on
mining opinions about entities in English language news, in which (a) we test
the relative suitability of various sentiment dictionaries and (b) we attempt
to separate positive or negative opinion from good or bad news. In the
experiments described here, we tested whether or not subject domain-defining
vocabulary should be ignored. Results showed that this idea is more appropriate
in the context of news opinion mining and that the approaches taking this into
consideration produce a better performance.

这篇论文研究了新闻文章情感分析的不同点，并且提出了三个需要解决的子任务，同时尝试从新闻文章中分离出正负面情感并挖掘其中的实体，最终得到的实验结果表明，忽略专业领域的词汇能够在新闻观点挖掘的任务中产生更好的效果。