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Learning Methods for Decentralized Control in Multi-Agent Systems

多智能体系统中分散控制的学习方法

基本信息

批准号：
2025732
负责人：
Ashutosh Nayyar
金额：
$ 40万
依托单位：
University of Southern California
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2025732&HistoricalAwards=false
关键词：
Learning Methods Decentralized Control Multi

项目摘要

Multi-agent systems (MAS) are expected to become increasingly prevalent in military and civilian domains. Decentralized control and decision-making by agents is a fundamental driver of the diverse applications of multi-agent systems. Agents are expected to act and make decisions without relying on a centralized command structure. Communication and coordination among agents may have to be carried out over sparse, intermittent, unreliable, low data rate and/or noisy communication networks that preclude the possibility of centralized information and decision-making. A key design challenge is to find efficient ways of computing decentralized control and decision strategies for a team of agents. The problem is further compounded by various kinds of uncertainties - uncertainties about the environment, noisy observations, unreliable communication as well as uncertainties in the system model. In this project, we aim to develop learning-based methods for decentralized control in multi-agent systems. Intellectual merit: The research develops the following: (i) learning-based practical methods for computing near-optimal decentralized control policies for multi-agent systems with known system model. (ii) online decentralized learning algorithms for control of multi-agent systems with unknown system model. We aim to develop decentralized algorithms that asymptotically find the optimal decentralized policy for such systems and learn in the most efficient way possible. The proposed research will lay the foundations for Learning-based Decentralized Optimal Control, which is expected to become increasingly important for emerging multi-agent system applications. Broader Impact: The research will significantly impact the science of multi-agent systems, autonomous robotic systems, and reinforcement learning. It will introduce a systematic and practical learning-based approach to design of multi-agent systems that has long been lacking in the literature. The educational impact of the proposed research will include: (i) providing graduate students with a multi-disciplinary training in stochastic control, online learning and optimization, (ii) involvement of undergraduate students during summer to perform computational and lab experiments (iii) efforts to recruit female and under-represented minority students in our projects; (iv) The research results will be incorporated in classes on reinforcement learning, stochastic systems, and decentralized control taught by the principal investigators.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

多智能体系统(MAS)有望在军事和民用领域变得越来越普遍。多智能体的分散控制和决策是多智能体系统多样化应用的根本驱动力。特工应该在不依赖中央指挥结构的情况下采取行动和做出决定。代理之间的通信和协调可能必须在稀疏、间断、不可靠、低数据速率和/或噪声的通信网络上进行，这排除了集中信息和决策的可能性。一个关键的设计挑战是找到有效的方法来计算一组代理的分散控制和决策策略。各种不确定因素进一步加剧了这个问题--环境的不确定因素、噪声观测、不可靠的通信以及系统模型中的不确定因素。在这个项目中，我们的目标是开发基于学习的方法来实现多智能体系统中的分散控制。(I)基于学习的实用方法，用于计算已知系统模型的多智能体系统的近似最优分散控制策略。(Ii)系统模型未知的多智能体系统控制的在线分散学习算法。我们的目标是开发分散算法，渐进地找到此类系统的最优分散策略，并以尽可能最有效的方式学习。所提出的研究将为基于学习的分散最优控制奠定基础，预计这将对新兴的多智能体系统的应用变得越来越重要。更广泛的影响：这项研究将对多智能体系统、自主机器人系统和强化学习的科学产生重大影响。它将介绍一种系统和实用的基于学习的方法来设计多代理系统，这是长期以来文献中所缺乏的。拟议研究的教育影响将包括：(I)为研究生提供随机控制、在线学习和优化方面的多学科培训；(Ii)让本科生在暑期进行计算和实验室实验；(Iii)努力在我们的项目中招募女性和代表性不足的少数族裔学生；(Iv)研究成果将被纳入主要研究人员教授的关于强化学习、随机系统和分散控制的课程。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。