最近已证明,平均场控制(MFC)是可扩展的工具,可近似解决大规模的多代理增强学习(MARL)问题。但是,这些研究通常仅限于无约束的累积奖励最大化框架。在本文中,我们表明,即使在存在约束的情况下,也可以使用MFC方法近似MARL问题。具体来说,我们证明,一个$ n $ agent的约束MARL问题,以及每个尺寸的尺寸$ | \ Mathcal {x} | $和$ | \ Mathcal {u} | $的状态和操作空间,可以通过与错误相关的约束MFC问题近似,$ e \ triangleq \ Mathcal {o} \ left([\ sqrt {| \ Mathcal {| \ Mathcal {x} |} |}+\ sqrt {| ]/\ sqrt {n} \ right)$。在奖励,成本和状态过渡功能独立于人口的行动分布的特殊情况下,我们证明该错误可以将错误提高到$ e = \ nathcal {o}(\ sqrt {| | \ Mathcal {x x x } |}/\ sqrt {n})$。另外,我们提供了一种基于自然策略梯度的算法,并证明它可以在$ \ Mathcal {o}(e)$的错误中解决受约束的MARL问题,并具有$ \ MATHCAL {O}的样本复杂性(E^{ - e^{ - 6})$。
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我们表明,在合作$ n $ n $ agent网络中,可以为代理设计本地可执行的策略,以使所得的平均奖励(值)的折现总和非常接近于计算出的最佳价值(包括非本地)策略。具体而言,我们证明,如果$ | \ MATHCAL {X} |,| \ MATHCAL {U} | $表示状态大小和单个代理的操作空间,那么对于足够小的折现因子,近似错误,则由$ \ MATHCAL {o}(e)$ where $ e \ triangleq \ frac {1} {\ sqrt {n}}} \ left [\ sqrt {\ sqrt {| \ Mathcal {x}} |} |} |} |}+\ sqrt { } |} \ right] $。此外,在一种特殊情况下,奖励和状态过渡功能独立于人口的行动分布,错误将$ \ nathcal {o}(e)$提高到其中$ e \ e \ triangleq \ frac {1} {\ sqrt {\ sqrt {n}} \ sqrt {| \ Mathcal {x} |} $。最后,我们还设计了一种算法来明确构建本地政策。在我们的近似结果的帮助下,我们进一步确定构建的本地策略在$ \ Mathcal {o}(\ max \ {e,\ epsilon \})$最佳策略的距离之内对于任何$ \ epsilon> 0 $,本地策略是$ \ MATHCAL {O}(\ Epsilon^{ - 3})$。
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本文提出了基于卷积神经网络的自动编码器(CNN-AE),以预测网络拓扑的位置依赖性率和覆盖率。我们训练CNN利用印度,巴西,德国和美国的BS位置数据,并将其性能与基于随机几何(SG)的分析模型进行比较。与最合适的SG模型相比,CNN-AE将覆盖范围和利率预测错误的利润分别提高到$ 40 \%$和$ 25 \%$。作为应用程序,我们提出了低复杂性,可证明是收敛的算法,使用经过训练的CNN-AE可以计算新的BS的位置,这些位置需要在网络中部署,以满足预定的空间异质性能目标。
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平均现场控制(MFC)是减轻合作多功能加强学习(MARL)问题的维度诅咒的有效方法。这项工作考虑了可以分离为$ k $课程的$ n _ {\ mathrm {pop}} $异质代理的集合,以便$ k $ -th类包含$ n_k $均匀的代理。我们的目标是通过其相应的MFC问题证明这一异构系统的Marl问题的近似保证。我们考虑三种情景,所有代理商的奖励和转型动态分别被视为$(1)美元的职能,每班的所有课程,$(2)美元和$(3) $边际分布的整个人口。我们展示,在这些情况下,$ k $ -class marl问题可以通过mfc近似于$ e_1 = mathcal {o}(\ frac {\ sqrt {| \ mathcal {x} |} + \ sqrt {| \ mathcal {u} |}}}}}} {n _ {\ mathrm {pop}}} \ sum_ {k} \ sqrt {k})$,$ e_2 = \ mathcal {o}(\ left [\ sqrt {| \ mathcal {x} |} + \ sqrt {| \ mathcal {u} |} \ \ sum_ {k} \ frac {1} {\ sqrt {n_k}})$和$ e_3 = \ mathcal {o} \ left(\ left [\ sqrt {| \ mathcal {x} |} + \ sqrt {| \ mathcal {} |} \ leftle] \ left [\ frac {a} {n _ {\ mathrm {pop}}} \ sum_ {k \在[k]}} \ sqrt {n_k} + \ frac {n} {\ sqrt {n} {\ sqrt {n \ mathrm {pop}}} \右] \ over)$,其中$ a,b $是一些常数和$ | mathcal {x} |,| \ mathcal {u} | $是每个代理的状态和行动空间的大小。最后,我们设计了一种基于自然的梯度(NPG)基于NPG的算法,它在上面规定的三种情况下,可以在$ \ Mathcal {O}(E_J)$错误中收敛到$ \ Mathcal的示例复杂度{ o}(e_j ^ { - 3})$,j \ in \ {1,2,3 \} $。
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量化城市道路网络(URNS)不同部分的拓扑相似之处使我们能够了解城市成长模式。虽然传统统计信息提供有关单个节点的直接邻居或整个网络的特性的有用信息,但是这种度量无法衡量考虑本地间接邻域关系的子网的相似性。在这项研究中,我们提出了一种基于图的机器学习方法来量化子网的空间均匀性。我们将该方法应用于全球30个城市的11,790个城市道路网络,以衡量每个城市和不同城市的道路网络的空间均匀性。我们发现,城市内的空间均匀性与诸如GDP和人口增长的社会经济地位高度相关。此外,通过在不同城市转移模型获得的城市间空间均匀性揭示了欧洲的城市网络结构的城市网络结构间相似性,传递给美国和亚洲的城市。可以利用使用我们的方法揭示的社会经济发展和城市间相似性,以了解和转移城市的洞察力。它还使我们能够解决城市政策挑战,包括在迅速城市化地区的网络规划,并打击区域不平等。
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Covid-19的反复暴发对全球社会产生了持久的影响,该社会呼吁使用具有早期可用性的各种数据来预测大流行波。现有的预测模型可以预测使用移动性数据的第一次爆发浪潮可能不适用于多波预测,因为美国和日本的证据表明,不同波浪之间的流动性模式在感染情况下与波动表现出不同的关系。因此,为了预测多波大流行,我们提出了一个基于社会意识的图形神经网络(SAB-GNN),它考虑了与症状相关的Web搜索频率的衰减,以捕获多个波浪中公共意识的变化。我们的模型结合了GNN和LSTM,以建模城市地区之间的复杂关系,跨区域的移动性模式,Web搜索历史记录和未来的Covid-19感染。我们训练我们的模型,从2020年4月至2021年5月,在雅虎日本公司根据严格的隐私保护规则中收集的四个大流行浪潮中,使用其移动性和Web搜索数据来预测东京地区的未来大流行爆发。结果证明了我们的模型优于最先进的基线,例如ST-GNN,MPNN和GraphLSTM。尽管我们的模型在计算上并不昂贵(只有3层和10个隐藏的神经元),但提出的模型使公共机构能够预料并为将来的大流行爆发做准备。
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In this paper we explore the task of modeling (semi) structured object sequences; in particular we focus our attention on the problem of developing a structure-aware input representation for such sequences. In such sequences, we assume that each structured object is represented by a set of key-value pairs which encode the attributes of the structured object. Given a universe of keys, a sequence of structured objects can then be viewed as an evolution of the values for each key, over time. We encode and construct a sequential representation using the values for a particular key (Temporal Value Modeling - TVM) and then self-attend over the set of key-conditioned value sequences to a create a representation of the structured object sequence (Key Aggregation - KA). We pre-train and fine-tune the two components independently and present an innovative training schedule that interleaves the training of both modules with shared attention heads. We find that this iterative two part-training results in better performance than a unified network with hierarchical encoding as well as over, other methods that use a {\em record-view} representation of the sequence \cite{de2021transformers4rec} or a simple {\em flattened} representation of the sequence. We conduct experiments using real-world data to demonstrate the advantage of interleaving TVM-KA on multiple tasks and detailed ablation studies motivating our modeling choices. We find that our approach performs better than flattening sequence objects and also allows us to operate on significantly larger sequences than existing methods.
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Optical coherence tomography (OCT) captures cross-sectional data and is used for the screening, monitoring, and treatment planning of retinal diseases. Technological developments to increase the speed of acquisition often results in systems with a narrower spectral bandwidth, and hence a lower axial resolution. Traditionally, image-processing-based techniques have been utilized to reconstruct subsampled OCT data and more recently, deep-learning-based methods have been explored. In this study, we simulate reduced axial scan (A-scan) resolution by Gaussian windowing in the spectral domain and investigate the use of a learning-based approach for image feature reconstruction. In anticipation of the reduced resolution that accompanies wide-field OCT systems, we build upon super-resolution techniques to explore methods to better aid clinicians in their decision-making to improve patient outcomes, by reconstructing lost features using a pixel-to-pixel approach with an altered super-resolution generative adversarial network (SRGAN) architecture.
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Real-life tools for decision-making in many critical domains are based on ranking results. With the increasing awareness of algorithmic fairness, recent works have presented measures for fairness in ranking. Many of those definitions consider the representation of different ``protected groups'', in the top-$k$ ranked items, for any reasonable $k$. Given the protected groups, confirming algorithmic fairness is a simple task. However, the groups' definitions may be unknown in advance. In this paper, we study the problem of detecting groups with biased representation in the top-$k$ ranked items, eliminating the need to pre-define protected groups. The number of such groups possible can be exponential, making the problem hard. We propose efficient search algorithms for two different fairness measures: global representation bounds, and proportional representation. Then we propose a method to explain the bias in the representations of groups utilizing the notion of Shapley values. We conclude with an experimental study, showing the scalability of our approach and demonstrating the usefulness of the proposed algorithms.
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The previous fine-grained datasets mainly focus on classification and are often captured in a controlled setup, with the camera focusing on the objects. We introduce the first Fine-Grained Vehicle Detection (FGVD) dataset in the wild, captured from a moving camera mounted on a car. It contains 5502 scene images with 210 unique fine-grained labels of multiple vehicle types organized in a three-level hierarchy. While previous classification datasets also include makes for different kinds of cars, the FGVD dataset introduces new class labels for categorizing two-wheelers, autorickshaws, and trucks. The FGVD dataset is challenging as it has vehicles in complex traffic scenarios with intra-class and inter-class variations in types, scale, pose, occlusion, and lighting conditions. The current object detectors like yolov5 and faster RCNN perform poorly on our dataset due to a lack of hierarchical modeling. Along with providing baseline results for existing object detectors on FGVD Dataset, we also present the results of a combination of an existing detector and the recent Hierarchical Residual Network (HRN) classifier for the FGVD task. Finally, we show that FGVD vehicle images are the most challenging to classify among the fine-grained datasets.
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