We study a multi-agent reinforcement learning (MARL) problem where the agents interact over a given network. The goal of the agents is to cooperatively maximize the average of their entropy-regularized long-term rewards. To overcome the curse of dimensionality and to reduce communication, we propose a Localized Policy Iteration (LPI) algorithm that provably learns a near-globally-optimal policy using only local information. In particular, we show that, despite restricting each agent's attention to only its $\kappa$-hop neighborhood, the agents are able to learn a policy with an optimality gap that decays polynomially in $\kappa$. In addition, we show the finite-sample convergence of LPI to the global optimal policy, which explicitly captures the trade-off between optimality and computational complexity in choosing $\kappa$. Numerical simulations demonstrate the effectiveness of LPI.

在这项工作中，我们研究了解决强化学习问题的基于政策的方法，其中采用了非政策性采样和线性函数近似进行政策评估，以及包括自然政策梯度（NPG）在内的各种政策更新规则，用于政策更新。为了在致命三合会的存在下解决政策评估子问题，我们提出了一个通用算法的多步型TD学习框架，具有广义的重要性抽样比率，其中包括两个特定的算法：$ \ lambda $ Q Q $ Q Q $ - 跟踪和双面$ Q $ - 跟踪。通用算法是单个时间尺度，具有可证明的有限样本保证，并克服了非政策学习中的高方差问题。至于策略更新，我们仅使用Bellman操作员的收缩属性和单调性属性提供通用分析，以在各种策略更新规则下建立几何融合。重要的是，通过将NPG视为实施政策迭代的近似方法，我们在不引入正则化的情况下建立了NPG的几何融合，并且不使用现有文献中的镜像下降类型的分析类型。将策略更新的几何融合与策略评估的有限样本分析相结合，我们首次建立了整​​体$ \ Mathcal {o}（\ Epsilon^{ - 2}）$样本复杂性以找到最佳策略（最多达到函数近似误差）使用基于策略的方法和线性函数近似下的基于策略的方法。

随机近似（SA）和随机梯度下降（SGD）算法是现代机器学习算法的工作马。由于快速收敛行为，它们在实践中优选它们的持续步骤变体。然而，恒定的步骤随机迭代算法不与最佳解决方案渐近地收敛，而是具有静止分布，这通常不能被分析表征。在这项工作中，我们研究了适当缩放的静止分布的渐近行为，在恒定步骤零的限制中。具体而言，我们考虑以下三种设置：（1）SGD算法，具有平滑且强的凸面物镜，（2）涉及Hurwitz矩阵的线性SA算法，和（3）涉及收缩算子的非线性SA算法。当迭代以$ 1 / \ sqrt {\ alpha} $缩放时，其中$ \ alpha $是常量的步骤，我们表明限制缩放静止分布是整体方程的解决方案。在该等式上的唯一性假设（可以在某些设置中除去），我们进一步表征了作为高斯分布的限制分布，其协方差矩阵是合适的Lyapunov方程的独特解决方案。对于超出这些情况的SA算法，我们的数值实验表明，与中央极限定理类型结果不同：（1）缩放因子不需要为$ 1 / \ sqrt {\ alpha} $，并且（2）限制分布不需要高斯。基于数值研究，我们提出了一种确定右缩放因子的公式，并与近似随机微分方程的欧拉 - 玛赖山离散化方案进行富有洞察力的连接。

重建3D对象是重要的计算机视觉任务，在AR/VR中具有广泛的应用。为此任务开发的深度学习算法通常依赖于不切实际的合成数据集，例如shapenet和things3d。另一方面，现有的以对象为中心的数据集通常没有足够的注释来实现监督培训或可靠的评估。在此技术报告中，我们提出了一个以照片为中心的对象数据集HM3D-ABO。它是通过构成现实的室内场景和现实对象来构建的。对于每种配置，我们提供多视图RGB观测值，这是对象，地面真实深度图和对象掩码的水密网格模型。所提出的数据集也可用于诸如摄像头估计和新颖视图合成之类的任务。数据集生成代码在https://github.com/zhenpeiyang/hm3d-abo上发布。

开发深度神经网络以生成3D场景是神经综合的基本问题，其立即应用于架构CAD，计算机图形，以及生成虚拟机器人训练环境。这项任务是具有挑战性的，因为3D场景呈现不同的模式，从连续的模式等等，例如对象尺寸和成对对之间的相对姿势，以离散模式，例如具有对称关系的对象的发生和共发生。本文介绍了一种新型神经场景综合方法，可以捕获3D场景的不同特征模式。我们的方法结合了神经网络和传统场景合成方法的强度。我们使用从训练数据中学到的参数上的分布，这提供了对象属性和相对属性的不确定性，以规范前馈神经模型的输出。此外，我们的方法不仅仅是预测场景布局，而不是预测场景布局。该方法允许我们利用预测属性之间的底层一致性约束来修剪不可行的预测。实验结果表明，我们的方法显着优于现有方法。生成的3D场景在保留连续和离散特征模式的同时忠实地插入训练数据。

Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention of previously seen data due to privacy regulations. In this work, to address such challenges, we study unsupervised segmentation in continual learning scenarios that involve domain shift. To that end, we introduce GarDA (Generative Appearance Replay for continual Domain Adaptation), a generative-replay based approach that can adapt a segmentation model sequentially to new domains with unlabeled data. In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains. Unlike previous approaches in incremental UDA, our method does not require access to previously seen data, making it applicable in many practical scenarios. We evaluate GarDA on two datasets with different organs and modalities, where it substantially outperforms existing techniques.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.

Compressed videos often exhibit visually annoying artifacts, known as Perceivable Encoding Artifacts (PEAs), which dramatically degrade video visual quality. Subjective and objective measures capable of identifying and quantifying various types of PEAs are critical in improving visual quality. In this paper, we investigate the influence of four spatial PEAs (i.e. blurring, blocking, bleeding, and ringing) and two temporal PEAs (i.e. flickering and floating) on video quality. For spatial artifacts, we propose a visual saliency model with a low computational cost and higher consistency with human visual perception. In terms of temporal artifacts, self-attention based TimeSFormer is improved to detect temporal artifacts. Based on the six types of PEAs, a quality metric called Saliency-Aware Spatio-Temporal Artifacts Measurement (SSTAM) is proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics. We believe that SSTAM will be beneficial for optimizing video coding techniques.

We propose a distributionally robust return-risk model for Markov decision processes (MDPs) under risk and reward ambiguity. The proposed model optimizes the weighted average of mean and percentile performances, and it covers the distributionally robust MDPs and the distributionally robust chance-constrained MDPs (both under reward ambiguity) as special cases. By considering that the unknown reward distribution lies in a Wasserstein ambiguity set, we derive the tractable reformulation for our model. In particular, we show that that the return-risk model can also account for risk from uncertain transition kernel when one only seeks deterministic policies, and that a distributionally robust MDP under the percentile criterion can be reformulated as its nominal counterpart at an adjusted risk level. A scalable first-order algorithm is designed to solve large-scale problems, and we demonstrate the advantages of our proposed model and algorithm through numerical experiments.