在最大的状态熵探索框架中，代理商与无奖励环境进行交互，以学习最大程度地提高其正在引起的预期国有访问的熵的政策。 Hazan等。 （2019年）指出，马尔可夫随机策略类别足以满足最大状态熵目标，而在这种情况下，利用非马克维亚性通常被认为是毫无意义的。在本文中，我们认为非马克维亚性是有限样本制度中最大状态熵探索至关重要的。尤其是，我们重新阐明了目标在一次试验中针对诱发的国有访问的预期熵的目标。然后，我们表明，非马克维亚确定性政策的类别足以满足引入的目标，而马尔可夫政策总体上遭受了非零的遗憾。但是，我们证明找到最佳的非马克维亚政策的问题是NP-HARD。尽管结果有负面的结果，但我们讨论了以一种可行的方式解决该问题的途径，以及非马克维亚探索如何使未来工作中在线增强学习的样本效率受益。

最近的几项工程致力于在一个环境中致力于无监督的加固学习，其中一项政策首先使用无监督的互动预测，然后微调在相同环境上定义的几个下游监督任务的最佳政策。沿着这一条线，我们解决了一类多种环境中无监督的加强学习问题，其中策略预先培训了从整个类的交互接受，然后在课堂的任何环境中进行微调。值得注意的是，问题本质上是多目标，因为我们可以在许多方面折交环境之间的预训练目标。在这项工作中，我们培养了对课堂内最不利的案件敏感的探索策略。因此，我们将探索问题作为勘探策略在整类环境中探索熵诱导的临界百分点的最大值的最大化。然后，我们提出了一种策略梯度算法，$ \ Alpha $ Mepol，通过与类的介导的交互来优化引入的目标。最后，我们经验展示了算法在学习探索挑战性的连续环境中的能力，我们展示了加强学习从预先接受训练的探索策略W.R.T.从头开始学习。

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.

Wireless Sensor Network (WSN) applications reshape the trend of warehouse monitoring systems allowing them to track and locate massive numbers of logistic entities in real-time. To support the tasks, classic Radio Frequency (RF)-based localization approaches (e.g. triangulation and trilateration) confront challenges due to multi-path fading and signal loss in noisy warehouse environment. In this paper, we investigate machine learning methods using a new grid-based WSN platform called Sensor Floor that can overcome the issues. Sensor Floor consists of 345 nodes installed across the floor of our logistic research hall with dual-band RF and Inertial Measurement Unit (IMU) sensors. Our goal is to localize all logistic entities, for this study we use a mobile robot. We record distributed sensing measurements of Received Signal Strength Indicator (RSSI) and IMU values as the dataset and position tracking from Vicon system as the ground truth. The asynchronous collected data is pre-processed and trained using Random Forest and Convolutional Neural Network (CNN). The CNN model with regularization outperforms the Random Forest in terms of localization accuracy with aproximate 15 cm. Moreover, the CNN architecture can be configured flexibly depending on the scenario in the warehouse. The hardware, software and the CNN architecture of the Sensor Floor are open-source under https://github.com/FLW-TUDO/sensorfloor.

One of the major challenges in Deep Reinforcement Learning for control is the need for extensive training to learn the policy. Motivated by this, we present the design of the Control-Tutored Deep Q-Networks (CT-DQN) algorithm, a Deep Reinforcement Learning algorithm that leverages a control tutor, i.e., an exogenous control law, to reduce learning time. The tutor can be designed using an approximate model of the system, without any assumption about the knowledge of the system's dynamics. There is no expectation that it will be able to achieve the control objective if used stand-alone. During learning, the tutor occasionally suggests an action, thus partially guiding exploration. We validate our approach on three scenarios from OpenAI Gym: the inverted pendulum, lunar lander, and car racing. We demonstrate that CT-DQN is able to achieve better or equivalent data efficiency with respect to the classic function approximation solutions.

To enable a safe and effective human-robot cooperation, it is crucial to develop models for the identification of human activities. Egocentric vision seems to be a viable solution to solve this problem, and therefore many works provide deep learning solutions to infer human actions from first person videos. However, although very promising, most of these do not consider the major challenges that comes with a realistic deployment, such as the portability of the model, the need for real-time inference, and the robustness with respect to the novel domains (i.e., new spaces, users, tasks). With this paper, we set the boundaries that egocentric vision models should consider for realistic applications, defining a novel setting of egocentric action recognition in the wild, which encourages researchers to develop novel, applications-aware solutions. We also present a new model-agnostic technique that enables the rapid repurposing of existing architectures in this new context, demonstrating the feasibility to deploy a model on a tiny device (Jetson Nano) and to perform the task directly on the edge with very low energy consumption (2.4W on average at 50 fps).

The Transformer architecture is shown to provide a powerful framework as an end-to-end model for building expression trees from online handwritten gestures corresponding to glyph strokes. In particular, the attention mechanism was successfully used to encode, learn and enforce the underlying syntax of expressions creating latent representations that are correctly decoded to the exact mathematical expression tree, providing robustness to ablated inputs and unseen glyphs. For the first time, the encoder is fed with spatio-temporal data tokens potentially forming an infinitely large vocabulary, which finds applications beyond that of online gesture recognition. A new supervised dataset of online handwriting gestures is provided for training models on generic handwriting recognition tasks and a new metric is proposed for the evaluation of the syntactic correctness of the output expression trees. A small Transformer model suitable for edge inference was successfully trained to an average normalised Levenshtein accuracy of 94%, resulting in valid postfix RPN tree representation for 94% of predictions.

网络流问题涉及通过网络分配流量，以便有效地使用基础基础架构，在运输和物流上无处不在。由于数据驱动的优化的吸引力，这些问题已越来越多地使用图形学习方法解决。其中，鉴于其通用性，多商品网络流（MCNF）问题特别感兴趣，因为它涉及多个来源和水槽之间不同大小的多个流量（也称为需求）的分布。我们关注的广泛使用的目标是给定流量需求和路由策略的网络中任何链接的最大利用。在本文中，我们针对MCNF问题提出了一种基于图形神经网络（GNN）的新方法，该方法沿每个链接使用明显的参数化消息函数，类似于所有边缘类型都是唯一的关系模型。我们表明，我们所提出的方法比现有的图形学习方法获得了可观的收益，这些方法不必要地限制了路由。我们使用17个服务提供商拓扑和两个流程路由方案通过互联网路由案例研究广泛评估所提出的方法。我们发现，在许多网络中，MLP与不使用我们机制的通用GNN具有竞争力。此外，我们阐明了图结构与数据驱动的流动路由的难度之间的关系，该方面在该地区现有工作中尚未考虑。

在本报告中，我们描述了我们提交给Epic-Kitchens-100无监督的域适应（UDA）挑战的技术细节。为了应对UDA设置下存在的域移位，我们首先利用了最近的域概括（DG）技术，称为相对规范对准（RNA）。其次，我们将这种方法扩展到无标记的目标数据工作，从而使模型更简单地以无监督的方式适应目标分布。为此，我们将UDA算法包括在内，例如多级对抗对准和专心熵。通过分析挑战设置，我们注意到数据中存在二次并发转移，通常称为环境偏见。它是由存在不同环境（即厨房）引起的。为了处理这两个班次（环境和时间段），我们扩展了系统以执行多源多目标域的适应性。最后，我们在最终提案中采用了不同的模型来利用流行视频体系结构的潜力，并为合奏改编介绍了两次损失。我们的提交（条目“ PLNET”）在排行榜上可见，并在“动词”中排名第二，并且在“名词”和“ Action”中都处于第三位。

端到端语音合成模型直接将输入字符转换为音频表示（例如频谱图）。尽管表现令人印象深刻，但此类模型仍很难消除相同拼写单词的发音。为了减轻此问题，可以在合成音频之前将单独的字素至phoneme（G2P）模型转换为音素。本文提出了SoundChoice，这是一种新颖的G2P体系结构，可以处理整个句子而不是在单词级别上操作。所提出的体系结构利用了加权同型损失（改善了歧义），利用课程学习（逐渐从单词级别切换到句子级别的G2P），并整合了Bert的单词嵌入（以进一步提高性能提高）。此外，该模型在语音识别中继承了最佳实践，包括使用Connectionist暂时分类（CTC）的多任务学习和带有嵌入式语言模型的光束搜索。结果，SoundChoice使用LibrisPeech和Wikipedia的数据实现了全句转录的音素错误率（PER），为2.65％。索引术语字素至音量，语音综合，文本传播，语音，发音，歧义。