EAGER- DynamicData: Novel Approaches for Optimization, Control, and Learning in Distributed Networks

传感器和机器人技术的最新进步导致了协调移动平台的大型网络，这些平台可以执行自动感知、地图绘制、学习和控制任务。虽然远程任务可以由地面中心通过远程通信链路进行控制，但这些链路昂贵且存在长时间延迟。该项目开发的计算方法将显著提高远程代理在本地相互协调执行任务的能力，这些任务包括识别和导航障碍物、重建信号以及从大量多模式传感器数据中学习新的控制策略，所有这些都只需很少或根本不与中心通信。建议的方法与目前的技术状态完全不同。它还包括课程、研讨会和针对代表性不足的少数群体和妇女的倡议等教育组成部分。拟议的工作是一套新的算法，用于解决多代理网络中的各种计算问题，用于涉及超大规模分布式数据集和高复杂性目标的问题，并且具有更高的精确度，其速度明显快于现有方法。已经取得了非常有希望的初步结果。提出的项目包括：(A)将多智能体协调与优化、博弈论、控制和学习中出现的问题集成到方程、包含或变分不等式系统中的新方法；(B)新的算子分裂方法，其导致这些系统的分散数值解，其规模、复杂性和多样性达到新的水平；(C)用于处理迫在眉睫的“分布式数据洪流”的随机逼近技术，以及基于方差减少、重要性采样和异步并行的加速技术；以及(D)一套针对动态和大规模数据的优化、控制和学习问题的开源软件产品，以及全面的评估计划。该项目的贡献是上文(A)和(B)部分中的统一框架，使分散的数值解能够以新的速度、复杂性、多样性和复原力水平进行。为了实现这些目标，将投入大量资源进行数学研究和工程挑战。

Recent advances in sensor and robotics technology have led to large networks of coordinated mobile platforms that can perform automatic sensing, mapping, learning, and control tasks. While the remote tasks can be controlled by the ground center via long-range communication links, the links are expensive and suffer long delays. This project develops computational methods that will significant improve the ability for the remote agents to coordinate locally with one another for tasks such as recognize and navigate around obstacles, reconstruct signals, and learn new control policies from a large amount of multi-modal sensor data, all done with little or no communication to a center. The proposed approach is radically different from the current state of the art. It also includes educational components such as courses, seminars, and initiatives for under-represented minority and women.The proposed work is a set of novel algorithms for a variety of computing problems in multi-agent networks, for problems involving extremely large-scale distributed datasets and high complexity objectives, and with greater accuracy at rates that are provably faster than existing methods. Very promising preliminary results have been obtained. The proposed project includes (a) a new approach to integrate multi-agent coordination with problems arising in optimization, game theory, control, and learning, into systems of equations, inclusions, or variational inequalities; (b) novel operator splitting methods that lead to decentralized numerical solutions of these systems, which scale to new levels of size, complexity, and diversity; (c) stochastic approximation techniques to deal with the imminent "distributed data deluge", along with accelerations techniques based on variance reduction, importance sampling, and asynchronous parallelization; and (d) a set of open-source software products for optimization, control, and learning problems with dynamic and large-scale data, along with a comprehensive evaluation plan. The contributions of the project is a unified framework in parts (a) and (b) above, which enable the decentralized numerical solutions at new levels of speed, complexity, diversity, and resilience. In order to achieve the goals, substantial resources will be devoted to both mathematical research and engineering challenges.