在将强化学习（RL）部署到现实世界系统中时，确保安全是一个至关重要的挑战。我们开发了基于置信的安全过滤器，这是一种基于概率动力学模型的标准RL技术，通过标准RL技术学到的名义策略来证明国家安全限制的控制理论方法。我们的方法基于对成本功能的国家约束的重新重新制定，从而将安全验证减少到标准RL任务。通过利用幻觉输入的概念，我们扩展了此公式，以确定对具有很高可能性的未知系统安全的“备份”策略。最后，在推出备用政策期间的每一个时间步骤中，标称政策的调整最少，以便以后可以保证安全恢复。我们提供正式的安全保证，并从经验上证明我们方法的有效性。

文本编辑模型最近已成为单语文本生成任务（例如语法误差校正，简化和样式传输）的SEQ2SEQ模型的突出替代方法。这些任务具有共同的特征 - 它们在源文本和目标文本之间表现出大量的文本重叠。文本编辑模型利用了此观察结果，并通过预测应用于源序列的编辑操作来学会生成输出。相比之下，Seq2Seq模型从头开始生成逐字输出，从而使它们在推理时间缓慢。文本编辑模型比SEQ2SEQ模型提供了多个好处，包括更快的推理速度，更高的样本效率以及对输出的更好的控制和解释性。本教程提供了有关文本编辑模型和当前最新方法的全面概述，并分析了他们的利弊。我们讨论了与生产化有关的挑战，以及如何使用这些模型来减轻幻觉和偏见，这两者都在文本生成领域遇到了紧迫的挑战。

我们以已知的奖励和未知的约束来研究顺序决策，这是由约束代表昂贵评估人类偏好（例如安全舒适的驾驶行为）的情况所激发的。我们将互动学习这些约束作为新的线性匪徒问题的挑战正式化，我们称之为约束的线性最佳臂识别。为了解决这个问题，我们提出了自适应约束学习（ACOL）算法。我们为约束线性最佳臂识别提供了一个依赖实例的下限，并表明Acol的样品复杂性与最坏情况下的下限匹配。在平均情况下，ACOL的样品复杂性结合仍然比简单方法的边界更紧密。在合成实验中，ACOL与Oracle溶液相同，并且表现优于一系列基准。作为应用程序，我们考虑学习限制，以代表驾驶模拟中的人类偏好。对于此应用，ACOL比替代方案要高得多。此外，我们发现学习偏好作为约束对驾驶场景的变化比直接编码奖励函数中的偏好更强大。

近年来，轨迹优化方法已在现实世界机器人上达到了出色的性能水平。这些方法在很大程度上依赖于动力学的准确分析模型，但是物理世界的某些方面只能在有限的程度上捕获。另一种方法是利用机器学习技术从数据中学习系统的可区分动力学模型。在这项工作中，我们使用轨迹优化和模型学习，在没有精确的动力学分析模型的情况下，使用机器人系统执行高度动态和复杂的任务。我们表明，从仅在两个不同的机器人上的25分钟相互作用的数据中收集的数据，神经网络可以准确地对高度非线性行为进行建模：（i）波士顿动力学点和（ii）RC CAR。此外，我们使用神经网络的梯度来执行基于梯度的轨迹优化。在我们的硬件实验中，我们证明了我们所学的模型可以代表现场和无线电控制（RC）汽车的复杂动力学，并与轨迹优化方法结合使用良好的性能。

本文提出了一个简单的食谱，用于训练最先进的多语言语法误差校正（GEC）模型。我们首先提出一种语言不足的方法来实现这一目标，以生成大量的合成示例。第二个成分是使用大规模的多语言模型（最多11B参数）。一旦对特定于语言的监督集进行了微调，我们就会以四种语言的GEC基准进行以前的最新结果：英语，捷克语，德语和俄语。在为GEC建立了一套新的基线后，我们通过释放Clang-8数据集使结果可以轻松地重现和访问。它是通过使用我们称为GT5的最佳型号来清洁广泛使用但嘈杂的Lang-8数据集的目标而产生的。 Clang-8极大地简化了由多个微调阶段组成的典型GEC训练管道 - 我们证明，使用现成的语言模型在Clang-8上执行单个微调步骤，可以进一步改善已经是顶级的，为英语执行GT5型号。

对于许多强化学习（RL）应用程序，指定奖励是困难的。本文考虑了一个RL设置，其中代理仅通过查询可以询问可以的专家来获取有关奖励的信息，例如，评估单个状态或通过轨迹提供二进制偏好。从如此昂贵的反馈中，我们的目标是学习奖励的模型，允许标准RL算法实现高预期的回报，尽可能少的专家查询。为此，我们提出了信息定向奖励学习（IDRL），它使用奖励的贝叶斯模型，然后选择要最大化信息增益的查询，这些查询是有关合理的最佳策略之间的返回差异的差异。与针对特定类型查询设计的先前主动奖励学习方法相比，IDRL自然地适应不同的查询类型。此外，它通过将焦点转移降低奖励近似误差来实现类似或更好的性能，从而降低奖励近似误差，以改善奖励模型引起的策略。我们支持我们的调查结果，在多个环境中进行广泛的评估，并具有不同的查询类型。

Logic Mill is a scalable and openly accessible software system that identifies semantically similar documents within either one domain-specific corpus or multi-domain corpora. It uses advanced Natural Language Processing (NLP) techniques to generate numerical representations of documents. Currently it leverages a large pre-trained language model to generate these document representations. The system focuses on scientific publications and patent documents and contains more than 200 million documents. It is easily accessible via a simple Application Programming Interface (API) or via a web interface. Moreover, it is continuously being updated and can be extended to text corpora from other domains. We see this system as a general-purpose tool for future research applications in the social sciences and other domains.

The analysis of network structure is essential to many scientific areas, ranging from biology to sociology. As the computational task of clustering these networks into partitions, i.e., solving the community detection problem, is generally NP-hard, heuristic solutions are indispensable. The exploration of expedient heuristics has led to the development of particularly promising approaches in the emerging technology of quantum computing. Motivated by the substantial hardware demands for all established quantum community detection approaches, we introduce a novel QUBO based approach that only needs number-of-nodes many qubits and is represented by a QUBO-matrix as sparse as the input graph's adjacency matrix. The substantial improvement on the sparsity of the QUBO-matrix, which is typically very dense in related work, is achieved through the novel concept of separation-nodes. Instead of assigning every node to a community directly, this approach relies on the identification of a separation-node set, which -- upon its removal from the graph -- yields a set of connected components, representing the core components of the communities. Employing a greedy heuristic to assign the nodes from the separation-node sets to the identified community cores, subsequent experimental results yield a proof of concept. This work hence displays a promising approach to NISQ ready quantum community detection, catalyzing the application of quantum computers for the network structure analysis of large scale, real world problem instances.

The following article presents a memetic algorithm with applying deep reinforcement learning (DRL) for solving practically oriented dual resource constrained flexible job shop scheduling problems (DRC-FJSSP). In recent years, there has been extensive research on DRL techniques, but without considering realistic, flexible and human-centered shopfloors. A research gap can be identified in the context of make-to-order oriented discontinuous manufacturing as it is often represented in medium-size companies with high service levels. From practical industry projects in this domain, we recognize requirements to depict flexible machines, human workers and capabilities, setup and processing operations, material arrival times, complex job paths with parallel tasks for bill of material (BOM) manufacturing, sequence-depended setup times and (partially) automated tasks. On the other hand, intensive research has been done on metaheuristics in the context of DRC-FJSSP. However, there is a lack of suitable and generic scheduling methods that can be holistically applied in sociotechnical production and assembly processes. In this paper, we first formulate an extended DRC-FJSSP induced by the practical requirements mentioned. Then we present our proposed hybrid framework with parallel computing for multicriteria optimization. Through numerical experiments with real-world data, we confirm that the framework generates feasible schedules efficiently and reliably. Utilizing DRL instead of random operations leads to better results and outperforms traditional approaches.

The acquisition of high-quality human annotations through crowdsourcing platforms like Amazon Mechanical Turk (MTurk) is more challenging than expected. The annotation quality might be affected by various aspects like annotation instructions, Human Intelligence Task (HIT) design, and wages paid to annotators, etc. To avoid potentially low-quality annotations which could mislead the evaluation of automatic summarization system outputs, we investigate the recruitment of high-quality MTurk workers via a three-step qualification pipeline. We show that we can successfully filter out bad workers before they carry out the evaluations and obtain high-quality annotations while optimizing the use of resources. This paper can serve as basis for the recruitment of qualified annotators in other challenging annotation tasks.