我们提出了一个基于最小描述长度（MDL）原理的多任务加固学习的新颖框架。在我们称MDL-Control（MDL-C）的这种方法中，代理商在面临的任务中学习了共同的结构，然后将其提炼成更简单的表示，从而促进更快的收敛性和对新任务的概括。这样一来，MDL-C自然将适应性适应与任务分布的认知不确定性平衡。我们通过MDL原理与贝叶斯推论之间的正式联系来激励MDL-C，得出理论性能保证，并在离散和高维连续控制任务上证明了MDL-C的经验有效性。从经验上讲，该框架用于修改现有的策略优化方法，并在离散和高维连续控制问题中改善其多任务性能。

深度加强学习的最近成功的大部分是由正常化的政策优化（RPO）算法驱动，具有跨多个域的强大性能。在这家族的方法中，代理经过培训，以在惩罚某些引用或默认策略的行为中的偏差时最大化累积奖励。除了经验的成功外，还有一个强大的理论基础，了解应用于单一任务的RPO方法，与自然梯度，信任区域和变分方法有关。但是，对于多任务设置中的默认策略，对所需属性的正式理解有限，越来越重要的域作为现场转向培训更有能力的代理商。在这里，我们通过将默认策略的质量与其对优化的影响正式链接到其对其影响的效果方面，进行第一步才能填补这种差距。使用这些结果，我们将获得具有强大性能保证的多任务学习的原则性的RPO算法。

动物和人工代理商都受益于支持跨任务的快速学习的国家表示，使他们能够有效地遍历其环境以获得奖励状态。在固定政策下衡量预期累积，贴现国家占用的后续代表（SR），可以在否则的马尔可维亚环境中有效地转移到不同的奖励结构，并假设生物行为和神经活动的基础方面。然而，在现实世界中，奖励可能会移动或仅用于消费一次，可能只是将位置或者代理可以简单地旨在尽可能快地到达目标状态，而不会产生人工强加的任务视野的约束。在这种情况下，最具行为相关的代表将携带有关代理人可能首先达到兴趣国的信息的信息，而不是在可能的无限时间跨度访问它们的频率。为了反映此类需求，我们介绍了第一次占用代表（FR），该代表（FR），该代表（FR）衡量预期的时间折扣首次访问状态。我们证明FR有助于探索，选择有效的路径到所需状态，允许代理在某些条件下规划由一系列子板定义的可透明的最佳轨迹，并引起避免威胁刺激的动物类似的行为。

Search and Rescue (SAR) missions in remote environments often employ autonomous multi-robot systems that learn, plan, and execute a combination of local single-robot control actions, group primitives, and global mission-oriented coordination and collaboration. Often, SAR coordination strategies are manually designed by human experts who can remotely control the multi-robot system and enable semi-autonomous operations. However, in remote environments where connectivity is limited and human intervention is often not possible, decentralized collaboration strategies are needed for fully-autonomous operations. Nevertheless, decentralized coordination may be ineffective in adversarial environments due to sensor noise, actuation faults, or manipulation of inter-agent communication data. In this paper, we propose an algorithmic approach based on adversarial multi-agent reinforcement learning (MARL) that allows robots to efficiently coordinate their strategies in the presence of adversarial inter-agent communications. In our setup, the objective of the multi-robot team is to discover targets strategically in an obstacle-strewn geographical area by minimizing the average time needed to find the targets. It is assumed that the robots have no prior knowledge of the target locations, and they can interact with only a subset of neighboring robots at any time. Based on the centralized training with decentralized execution (CTDE) paradigm in MARL, we utilize a hierarchical meta-learning framework to learn dynamic team-coordination modalities and discover emergent team behavior under complex cooperative-competitive scenarios. The effectiveness of our approach is demonstrated on a collection of prototype grid-world environments with different specifications of benign and adversarial agents, target locations, and agent rewards.

By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specific datasets to a high level of performance. While this capability has been demonstrated in other fields such as computer vision, natural language processing or speech recognition, it remains to be shown in robotics, where the generalization capabilities of the models are particularly critical due to the difficulty of collecting real-world robotic data. We argue that one of the keys to the success of such general robotic models lies with open-ended task-agnostic training, combined with high-capacity architectures that can absorb all of the diverse, robotic data. In this paper, we present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties. We verify our conclusions in a study of different model classes and their ability to generalize as a function of the data size, model size, and data diversity based on a large-scale data collection on real robots performing real-world tasks. The project's website and videos can be found at robotics-transformer.github.io

This work addresses cross-view camera pose estimation, i.e., determining the 3-DoF camera pose of a given ground-level image w.r.t. an aerial image of the local area. We propose SliceMatch, which consists of ground and aerial feature extractors, feature aggregators, and a pose predictor. The feature extractors extract dense features from the ground and aerial images. Given a set of candidate camera poses, the feature aggregators construct a single ground descriptor and a set of rotational equivariant pose-dependent aerial descriptors. Notably, our novel aerial feature aggregator has a cross-view attention module for ground-view guided aerial feature selection, and utilizes the geometric projection of the ground camera's viewing frustum on the aerial image to pool features. The efficient construction of aerial descriptors is achieved by using precomputed masks and by re-assembling the aerial descriptors for rotated poses. SliceMatch is trained using contrastive learning and pose estimation is formulated as a similarity comparison between the ground descriptor and the aerial descriptors. SliceMatch outperforms the state-of-the-art by 19% and 62% in median localization error on the VIGOR and KITTI datasets, with 3x FPS of the fastest baseline.

This paper is a technical overview of DeepMind and Google's recent work on reinforcement learning for controlling commercial cooling systems. Building on expertise that began with cooling Google's data centers more efficiently, we recently conducted live experiments on two real-world facilities in partnership with Trane Technologies, a building management system provider. These live experiments had a variety of challenges in areas such as evaluation, learning from offline data, and constraint satisfaction. Our paper describes these challenges in the hope that awareness of them will benefit future applied RL work. We also describe the way we adapted our RL system to deal with these challenges, resulting in energy savings of approximately 9% and 13% respectively at the two live experiment sites.

Grammatical Error Correction (GEC) is the task of automatically detecting and correcting errors in text. The task not only includes the correction of grammatical errors, such as missing prepositions and mismatched subject-verb agreement, but also orthographic and semantic errors, such as misspellings and word choice errors respectively. The field has seen significant progress in the last decade, motivated in part by a series of five shared tasks, which drove the development of rule-based methods, statistical classifiers, statistical machine translation, and finally neural machine translation systems which represent the current dominant state of the art. In this survey paper, we condense the field into a single article and first outline some of the linguistic challenges of the task, introduce the most popular datasets that are available to researchers (for both English and other languages), and summarise the various methods and techniques that have been developed with a particular focus on artificial error generation. We next describe the many different approaches to evaluation as well as concerns surrounding metric reliability, especially in relation to subjective human judgements, before concluding with an overview of recent progress and suggestions for future work and remaining challenges. We hope that this survey will serve as comprehensive resource for researchers who are new to the field or who want to be kept apprised of recent developments.

The computational complexity of the self-attention mechanism in Transformer models significantly limits their ability to generalize over long temporal durations. Memory-augmentation, or the explicit storing of past information in external memory for subsequent predictions, has become a constructive avenue for mitigating this limitation. We argue that memory-augmented Transformers can benefit substantially from considering insights from the memory literature in humans. We detail an approach for integrating evidence from the human memory system through the specification of cross-domain linking hypotheses. We then provide an empirical demonstration to evaluate the use of surprisal as a linking hypothesis, and further identify the limitations of this approach to inform future research.

通过将从地面视图摄像头拍摄到从卫星或飞机上拍摄的架空图像的图像，通过将代理定位在搜索区域内，将代理定位在搜索区域内，将代理定位在搜索区域中。尽管地面图像和架空图像之间的观点差异使得跨视图地理定位具有挑战性，但假设地面代理可以使用全景相机，则取得了重大进展。例如，我们先前的工作（WAG）引入了搜索区域离散化，训练损失和粒子过滤器加权的变化，从而实现了城市规模的全景跨视图地理定位。但是，由于其复杂性和成本，全景相机并未在现有机器人平台中广泛使用。非Panoramic跨视图地理定位更适用于机器人技术，但也更具挑战性。本文介绍了受限的FOV广泛地理定位（Rewag），这是一种跨视图地理定位方法，通过创建姿势吸引的嵌入并提供将粒子姿势纳入暹罗网络，将其概括为与标准的非填充地面摄像机一起使用，以供与标准的非卧型地面摄像机一起使用。 Rewag是一种神经网络和粒子滤波器系统，能够在GPS下的环境中全球定位移动代理，仅具有探测仪和90度FOV摄像机，其本地化精度与使用全景相机实现并提高本地化精度相似的定位精度与基线视觉变压器（VIT）方法相比，100倍。一个视频亮点，该视频亮点在https://youtu.be/u_obqrt8qce上展示了几十公里的测试路径上的收敛。