本文提出了一种终身学习复发性神经网络的方法，例如NNARX，ESN，LSTM和GRU，在控制系统合成中被用作植物模型。该问题很重要，因为在许多实际应用中，需要在可用的新信息和/或系统进行更改时调整模型，而无需随时存储越来越多的数据。确实，在这种情况下，出现了许多问题，例如众所周知的灾难性遗忘和容量饱和。我们提出了一种受移动范围估计器启发的适应算法，从而得出了其收敛条件。所描述的方法应用于现有文献中已经具有挑战性的基准的模拟化学厂。讨论了获得的主要结果。

本文介绍了非线性MPC控制器的设计，该设计为通过神经非线性自动回归外源性（NNARX）网络描述的模型提供无抵销的设定值跟踪。 NNARX模型是从工厂收集的输入输出数据中标识的，并且可以通过过去的输入和输出变量为已知的可测量状态给出状态空间表示，因此不需要状态观察者。在训练阶段，与工厂行为一致时，可以强制强制强制输入到国家稳定性（{\ delta} ISS）属性。然后，利用{\ delta} ISS属性在输出跟踪误差上采取明确的积分操作来增强模型，从而可以实现为设计的控制方案实现无抵销的跟踪功能。在水加热系统上进行了数值测试，并将所达到的结果与另一种流行的无偏移MPC方法评分的结果进行了数值测试，这表明即使在植物上作用着骚动，提出的方案也达到了显着的性能。

本文旨在讨论和分析控制设计应用中经常性神经网络（RNN）的潜力。考虑RNN的主要系列，即神经非线性自回归外源，（NNARX），回波状态网络（ESN），长短短期存储器（LSTM）和门控复发单元（GRU）。目标是双重。首先，为了调查近期RNN培训的结果，可以享受输入到状态稳定性（ISS）和增量输入到状态稳定性（{\ delta} ISS）保证。其次，讨论仍然阻碍RNN进行控制的问题，即它们的鲁棒性，核算和解释性。前者属性与网络的所谓概括能力有关，即即使在视野或扰动的输入轨迹存在下，它们与底层真实植物的一致性。后者与在RNN模型和植物之间提供明确的正式连接的可能性有关。在这种情况下，我们说明了Iss和{\ delta} ISS如何朝着RNN模型的稳健性和可验证代表重大步骤，而可解释性的要求铺平了基于物理的网络的使用方式。还简要讨论了植物模型的模型预测控制器的设计。最后，在模拟化学体系上说明了本文的一些主要话题。

Autoregressive processes naturally arise in a large variety of real-world scenarios, including e.g., stock markets, sell forecasting, weather prediction, advertising, and pricing. When addressing a sequential decision-making problem in such a context, the temporal dependence between consecutive observations should be properly accounted for converge to the optimal decision policy. In this work, we propose a novel online learning setting, named Autoregressive Bandits (ARBs), in which the observed reward follows an autoregressive process of order $k$, whose parameters depend on the action the agent chooses, within a finite set of $n$ actions. Then, we devise an optimistic regret minimization algorithm AutoRegressive Upper Confidence Bounds (AR-UCB) that suffers regret of order $\widetilde{\mathcal{O}} \left( \frac{(k+1)^{3/2}\sqrt{nT}}{(1-\Gamma)^2} \right)$, being $T$ the optimization horizon and $\Gamma < 1$ an index of the stability of the system. Finally, we present a numerical validation in several synthetic and one real-world setting, in comparison with general and specific purpose bandit baselines showing the advantages of the proposed approach.

Behavioral Cloning (BC) aims at learning a policy that mimics the behavior demonstrated by an expert. The current theoretical understanding of BC is limited to the case of finite actions. In this paper, we study BC with the goal of providing theoretical guarantees on the performance of the imitator policy in the case of continuous actions. We start by deriving a novel bound on the performance gap based on Wasserstein distance, applicable for continuous-action experts, holding under the assumption that the value function is Lipschitz continuous. Since this latter condition is hardy fulfilled in practice, even for Lipschitz Markov Decision Processes and policies, we propose a relaxed setting, proving that value function is always Holder continuous. This result is of independent interest and allows obtaining in BC a general bound for the performance of the imitator policy. Finally, we analyze noise injection, a common practice in which the expert action is executed in the environment after the application of a noise kernel. We show that this practice allows deriving stronger performance guarantees, at the price of a bias due to the noise addition.

This paper is in the field of stochastic Multi-Armed Bandits (MABs), i.e., those sequential selection techniques able to learn online using only the feedback given by the chosen option (a.k.a. arm). We study a particular case of the rested and restless bandits in which the arms' expected payoff is monotonically non-decreasing. This characteristic allows designing specifically crafted algorithms that exploit the regularity of the payoffs to provide tight regret bounds. We design an algorithm for the rested case (R-ed-UCB) and one for the restless case (R-less-UCB), providing a regret bound depending on the properties of the instance and, under certain circumstances, of $\widetilde{\mathcal{O}}(T^{\frac{2}{3}})$. We empirically compare our algorithms with state-of-the-art methods for non-stationary MABs over several synthetically generated tasks and an online model selection problem for a real-world dataset. Finally, using synthetic and real-world data, we illustrate the effectiveness of the proposed approaches compared with state-of-the-art algorithms for the non-stationary bandits.

Robot assistants are emerging as high-tech solutions to support people in everyday life. Following and assisting the user in the domestic environment requires flexible mobility to safely move in cluttered spaces. We introduce a new approach to person following for assistance and monitoring. Our methodology exploits an omnidirectional robotic platform to detach the computation of linear and angular velocities and navigate within the domestic environment without losing track of the assisted person. While linear velocities are managed by a conventional Dynamic Window Approach (DWA) local planner, we trained a Deep Reinforcement Learning (DRL) agent to predict optimized angular velocities commands and maintain the orientation of the robot towards the user. We evaluate our navigation system on a real omnidirectional platform in various indoor scenarios, demonstrating the competitive advantage of our solution compared to a standard differential steering following.

我们假设现有的句子级机器翻译（MT）指标在人类参考包含歧义时会效率降低。为了验证这一假设，我们提出了一种非常简单的方法，用于扩展预审计的指标以在文档级别合并上下文。我们将我们的方法应用于三个流行的指标，即Bertscore，Prism和Comet，以及无参考的公制Comet-QE。我们使用提供的MQM注释评估WMT 2021指标共享任务的扩展指标。我们的结果表明，扩展指标的表现在约85％的测试条件下优于其句子级别的级别，而在排除低质量人类参考的结果时。此外，我们表明我们的文档级扩展大大提高了其对话语现象任务的准确性，从而优于专用基线高达6.1％。我们的实验结果支持我们的初始假设，并表明对指标的简单扩展使他们能够利用上下文来解决参考中的歧义。

本文描述了可以用于控制上限假体的人机界面的新框架。目的是从嘈杂的表面肌电图信号中估算人类的电动机意图，并在存在以前看不见的扰动的情况下，对假体（即机器人）执行电动机意图。该框架包括每个自由度的肌肉弯曲模型，一种学习用于估计用户电机意图的模型的参数值的方法，以及使用从肌肉模型获得的刚度和阻尼值来适应的可变阻抗控制器假体运动轨迹和动力学。我们使用人机界面的模拟版本在强大的人类的背景下进行实验评估我们的框架，以执行主要在手腕中攻击一种自由的任务，并以统一力场的形式考虑外部扰动这将手腕从目标上推开。我们证明我们的框架提供了所需的自适应性能，并且与数据驱动的基线相比，可以大大提高性能。

知识图（kgs）已被证明是构建数据的可靠方法。他们可以提供有关文化遗产收藏的丰富情境信息。但是，文化遗产库库远非完整。他们通常会缺少重要的属性，例如地理位置，尤其是对于雕塑，移动或室内实体，例如绘画。在本文中，我们首先提出了一个框架，用于从各种数据源及其连接的多跳知识中汲取有关有形文化遗产实体的知识。其次，我们提出了一个多视图学习模型，用于估计给定的文化遗产实体之间的相对距离，该模型基于实体的地理和知识联系。