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An Optimal Control-Based Distributed Reinforcement Learning Framework for A Class of Non-Convex Objective Functionals of the Multi-Agent Network

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自动化学报:英文版 2023年第11期10卷 2081-2093页

作者： Zhe Chen Ning Li Department of Automation Shanghai Jiao Tong UniversityShanghai 200240 Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240 Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Department of Automation Tsinghua UniversityBeijing 100084China IEEE

This paper studies a novel distributed optimization problem that aims to minimize the sum of the non-convex objective functionals of the Multi-Agent network under privacy protection, which means that the local objective of each agent is unknown to others. The above problem involves complexity simultaneously in the time and space aspects. Yet existing works about distributed optimization mainly consider privacy protection in the space aspect where the decision variable is a vector with finite dimensions. In contrast, when the time aspect is considered in this paper, the decision variable is a continuous function concerning time. Hence, the minimization of the overall functional belongs to the calculus of variations. Traditional works usually aim to seek the optimal decision function. Due to privacy protection and non-convexity, the Euler-Lagrange equation of the proposed problem is a complicated partial differential equation.Hence, we seek the optimal decision derivative function rather than the decision function. This manner can be regarded as seeking the control input for an optimal control problem, for which we propose a centralized reinforcement learning(RL) framework. In the space aspect, we further present a distributed reinforcement learning framework to deal with the impact of privacy protection. Finally, rigorous theoretical analysis and simulation validate the effectiveness of our framework.

关键词： Distributed optimization Multi-Agent optimal control reinforcement learning(RL)

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基于Multi-Agent的无人机集群体系自主作战系统设计

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系统工程与电子技术 2023年

作者：张堃华帅袁斌林杜睿怡西北工业大学电子信息学院国防科技大学空天科学学院光电控制技术重点实验室

针对无人集群自主作战体系设计关键问题，提出基于Multi-Agent的无人集群自主作战系统设计方法，建立无人集群各节点的agent模型及其推演规则；对于仿真系统模块化和通用化的需求，设计系统互操作式接口和无人机集群自主作战的交互关系... 详细信息

针对无人集群自主作战体系设计关键问题，提出基于Multi-Agent的无人集群自主作战系统设计方法，建立无人集群各节点的agent模型及其推演规则；对于仿真系统模块化和通用化的需求，设计系统互操作式接口和无人机集群自主作战的交互关系；开展无人集群系统仿真推演验证，结果表明，所提设计方案不仅能够有效开展并完成自主作战网络生成-集群演化-效能评估的全过程动态演示验证，而且能够通过重复随机试验进一步评估无人机集群的协同作战效能，总结集群协同作战的策略和经验。

关键词： Multi-Agent 无人集群体系设计协同作战

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Application-Oriented Homogeneous Control Protocol Design for Multi-Agent Systems Under Input Constraints

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Guidance, Navigation and Control 2023年第3期3卷 89-109页

作者： Siyuan Wang Sandeep Kumar Soni Gang Zheng Driss Boutat Department of Energy Risks and Environment INSA Centre Val de LoireBourges18000France Department of Electrical Engineering Shiv Nadar Institution of Eminence Delhi NCR201314India Defrost Centre Inria de l'Universite de Lille Villeneuve-d'Ascq59650France

An application-oriented homogeneous control protocol is proposed to solve the consensus problem of Multi-Agent system(MAS)modeled by higher-order integrator.This nonlinear control protocol is able to homogenize the linear system with a special degree called homogeneity degree.This homogeneous control protocol ensures asymptotically/finite-time stable Multi-Agent systems(MASs)(or fixed-time attractive to compact sets containing the origin)by selecting di®erent homogeneity degrees.If the linear control protocol for each agent is provided,the proposed nonlinear homogeneous control protocol in this paper can be easily implemented without requiring any tuning of the control parameters.A bounded homogeneous control protocol,which is a special form of the controller proposed,is also introduced to address the same problem with input constraints.Finally,numerical simulations are conducted to demonstrate the e®ectiveness of the proposed approach.

关键词： Homogeneous system Multi-Agent input constraint

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Multi-Agent system application in accordance with game theory in bi-directional coordination network model

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Journal of Systems Engineering and Electronics 2020年第2期31卷 279-289页

作者： ZHANG Jie WANG Gang YUE Shaohua SONG Yafei LIU Jiayi YAO Xiaoqiang College of Electronics and Information Engineering Air Force Engineering UniversityXi’an 710054China College of Air Missile Defense Air Force Engineering UniversityXi’an 710054China

The Multi-Agent system is the optimal solution to complex intelligent problems. In accordance with the game theory, the concept of loyalty is introduced to analyze the relationship between agents' individual income and global benefits and build the logical architecture of the Multi-Agent system. Besides, to verify the feasibility of the method, the cyclic neural network is optimized, the bi-directional coordination network is built as the training network for deep learning, and specific training scenes are simulated as the training background. After a certain number of training iterations, the model can learn simple strategies autonomously. Also,as the training time increases, the complexity of learning strategies rises gradually. Strategies such as obstacle avoidance, firepower distribution and collaborative cover are adopted to demonstrate the achievability of the model. The model is verified to be realizable by the examples of obstacle avoidance, fire distribution and cooperative cover. Under the same resource background, the model exhibits better convergence than other deep learning training networks, and it is not easy to fall into the local endless loop.Furthermore, the ability of the learning strategy is stronger than that of the training model based on rules, which is of great practical values.

关键词： loyalty game theory bi-directional coordination network Multi-Agent system learning strategy

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Consensus Control With a Constant Gain for Discrete-time Binary-valued Multi-Agent Systems Based on a Projected Empirical Measure Method

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IEEE/CAA Journal of Automatica Sinica 2019年第4期6卷 1052-1059页

作者： Ting Wang Min Hu Yanlong Zhao IEEE the Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China the School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100149China

This paper studies the consensus control of multiagent systems with binary-valued observations.An algorithm alternating estimation and control is proposed.Each agent estimates the states of its neighbors based on a projected empirical measure method for a holding time.Based on the estimates,each agent designs the consensus control with a constant gain at some skipping time.The states of the system are updated by the designed control,and the estimation and control design will be repeated.For the estimation,the projected empirical measure method is proposed for the binary-valued observations.The algorithm can ensure the uniform boundedness of the estimates and the mean square error of the estimation is proved to be at the order of the reciprocal of the holding time(the same order as that in the case of accurate outputs).For the consensus control,a constant gain is designed instead of the stochastic approximation based gain in the existing literature for binary-valued observations.And,there is no need to make modification for control since the uniform boundedness of the estimates ensures the uniform boundedness of the agents’states.Finally,the systems updated by the designed control are proved to achieve consensus and the consensus speed is faster than that in the existing literature.Simulations are given to demonstrate the theoretical results.

关键词： Binary-valued observations consensus control constant gain convergence rate Multi-Agent systems projected empirical measure method

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Leader-following consensus of second-order nonlinear Multi-Agent systems subject to disturbances

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Frontiers of Information Technology & Electronic Engineering 2019年第1期20卷 88-94页

作者： Mao-bin LU Lu LIU School of Automation Beijing Institute of Technology City University of Hong Kong Shenzhen Research Institute Department of Biomedical Engineering City University of Hong Kong

In this study, we investigate the leader-following consensus problem of a class of heterogeneous secondorder nonlinear Multi-Agent systems subject to disturbances. In particular, the nonlinear systems contain uncertainties that can be linearly parameterized. We propose a class of novel distributed control laws, which depends on the relative state of the system and thus can be implemented even when no communication among agents exists. By Barbalat's lemma, we demonstrate that consensus of the second-order nonlinear Multi-Agent system can be achieved by the proposed distributed control law. The effectiveness of the main result is verified by its application to consensus control of a group of Van der Pol oscillators.

关键词： Multi-Agent systems Leader-following consensus Distributed control

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基于Multi-Agent的分布式文本聚类模型

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计算机学报 2018年第8期41卷 1709-1721页

作者：乔少杰韩楠金澈清高云君李天瑞唐常杰康健成都信息工程大学网络空间安全学院成都610225 成都信息工程大学管理学院成都610103 华东师范大学数据科学与工程学院华东师范大学计算机科学与软件工程学院上海200062 浙江大学计算机科学与技术学院杭州310027 西南交通大学信息科学与技术学院成都611756 四川大学计算机学院成都610065

Internet网络大数据与日俱增,当前亟需设计出能够处理大规模半结构化和无结构化文本数据的新型聚类方法.现有工作的不足体现在:应用的文本集较为单一,对半结构和无结构的Web文本进行聚类的准确性较低,当文档规模较大时聚类的时效性无法... 详细信息

Internet网络大数据与日俱增,当前亟需设计出能够处理大规模半结构化和无结构化文本数据的新型聚类方法.现有工作的不足体现在:应用的文本集较为单一,对半结构和无结构的Web文本进行聚类的准确性较低,当文档规模较大时聚类的时效性无法得到保证.针对上述不足,提出新的基于群体智能的文本聚类模型Switch(a Swarm intelligence based text clustering algorithm),支持包括藏文、汉文、英文等多语言的文本聚类.基本思想为:构建文本的向量空间模型,借助自然语言处理和数据预处理技术得到由特征向量构成的文本集合;对群体智能文本聚类算法的参数进行初始化,不同智能体可以在二维文本空间上任意移动,计算其所在网格区域文本与其他样本的相似度,利用概率转换函数求取智能体拿起和放下样本的概率,进而实现文本聚类.提出分布式动态文本流聚类的Multi-Agent架构,将这一架构应用于群体智能文本聚类算法中,分布式工作环境被设计成相互通信的软agents集合,设计了相似度计算,智能体状态感知,文本解析三类智能体.通过解决智能体状态同步、处理器负载均衡和处理器之间通信的代价问题,将计算任务分成不同子任务,在多处理器上分布执行.此外,阐述了基于Multi-Agent的分布式群体智能文本聚类方法的工作原理,给出一种分布式通信架构,各种智能体相互通信,相互协作完成文本聚类工作.基于Multi-Agent通过JADE(Java agent Development Framework)中间件实现集群上的分布式文本聚类,优势在于:分布式计算和大内存处理较单机具有更好的处理能力,借助JADE中间件能够使智能体间相互通信及协作,实现高效的文本聚类.在大量真实的半结构化包含藏文、汉文和英文多语言的Web文本数据集上进行实验,以藏文为例,实验结果表明:相比于k-means和单节点上的群体智能聚类算法,提出的分布式架构下文本聚类算法准确性平均高出12.2%和3.8%,时间代价平均缩减了73.0%和50.6%.在n个节点集群下agents数量介于150~250之间时,文本聚类时间代价近似可以达到单节点的1/n.

关键词： Multi-Agent 分布式架构群体智能文本聚类

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A Multi-Agent architecture for scheduling in platform-based smart manufacturing systems

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Frontiers of Information Technology & Electronic Engineering 2019年第11期20卷 1465-1492页

作者： Yong-kui LIU Xue-song ZHANG Lin ZHANG Fei TAO Li-hui WANG Center for Intelligent Manufacturing Systems and Robots School of Mechano-Electronic EngineeringXidian UniversityXi'an 710071China College of Computer Science and Technology Jilin UniversityChangchun 130012China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Department of Production Engineering KTH Royal Institute of TechnologyStockholm 11428Sweden

During the past years, a number of smart manufacturing concepts have been proposed, such as cloud manufacturing, Industry 4.0, and Industrial Internet. One of their common aims is to optimize the collaborative resource configuration across enterprises by establishing platforms that aggregate distributed resources. In all of these concepts, a complete manufacturing system consists of distributed physical manufacturing systems and a platform containing the virtual manufacturing systems mapped from the physical ones. We call such manufacturing systems platform-based smart manufacturing systems (PSMSs). A PSMS can therefore be regarded as a huge cyber-physical system with the cyber part being the platform and the physical part being the corresponding physical manufacturing system. A significant issue for a PSMS is how to optimally schedule the aggregated resources. Multi-Agent technology provides an effective approach for solving this issue. In this paper we propose a Multi-Agent architecture for scheduling in PSMSs, which consists of a platform-level scheduling Multi-Agent system (MAS) and an enterprise-level scheduling MAS. Procedures, characteristics, and requirements of scheduling in PSMSs are presented. A model for sched-uling in a PSMS based on the architecture is proposed. A case study is conducted to demonstrate the effectiveness of the proposed architecture and model.

关键词： Platform Smart manufacturing Multi-Agent Scheduling

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Time-varying formation tracking for uncertain second-order nonlinear Multi-Agent systems

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Frontiers of Information Technology & Electronic Engineering 2019年第1期20卷 76-87页

作者： Mao-peng RAN Li-hua XIE Jun-cheng LI School of Electrical and Electronic Engineering Nanyang Technological University

Our study is concerned with the time-varying formation tracking problem for second-order Multi-Agent systems that are subject to unknown nonlinear dynamics and external disturbance, and the states of the followers form a predefined time-varying formation while tracking the state of the leader. The total uncertainty lumps the unknown nonlinear dynamics and the external disturbance, and is regarded as an extended state of the agent. To estimate the total uncertainty, we design an extended state observer(ESO). Then we propose a novel ESO based time-varying formation tracking protocol. It is proved that, under the proposed protocol, the ESO estimation error and the time-varying formation tracking error can be made arbitrarily small. An application to the target enclosing problem for multiple unmanned aerial vehicles(UAVs) verifies the effectiveness and superiority of the proposed approach.

关键词： Multi-Agent system Time-varying formation Formation tracking Nonlinear dynamics Extended state observer(ESO)

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Resource Allocation and Power Control Policy for Device-to-Device Communication Using Multi-Agent Reinforcement Learning

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Computers, Materials & Continua 2020年第6期63卷 1515-1532页

作者： Yifei Wei Yinxiang Qu Min Zhao Lianping Zhang F.Richard Yu Beijing Key Laboratory of Work Safety Intelligent Monitoring Beijing University of Posts and TelecommunicationsBeijing100876China Alibaba Cloud Computing Hangzhou311121China Department of Systems and Computer Engineering Carleton UniversityOttawaK1S 5B6Canada

Device-to-Device(D2D)communication is a promising technology that can reduce the burden on cellular networks while increasing network capacity.In this paper,we focus on the channel resource allocation and power control to improve the system resource utilization and network throughput.Firstly,we treat each D2D pair as an independent agent.Each agent makes decisions based on the local channel states information observed by itself.The Multi-Agent Reinforcement Learning(RL)algorithm is proposed for our multi-user system.We assume that the D2D pair do not possess any information on the availability and quality of the resource block to be selected,so the problem is modeled as a stochastic non-cooperative game.Hence,each agent becomes a player and they make decisions together to achieve global optimization.Thereby,the Multi-Agent Q-learning algorithm based on game theory is established.Secondly,in order to accelerate the convergence rate of Multi-Agent Q-learning,we consider a power allocation strategy based on Fuzzy C-means(FCM)algorithm.The strategy firstly groups the D2D users by FCM,and treats each group as an agent,and then performs Multi-Agent Q-learning algorithm to determine the power for each group of D2D users.The simulation results show that the Q-learning algorithm based on Multi-Agent can improve the throughput of the system.In particular,FCM can greatly speed up the convergence of the Multi-Agent Q-learning algorithm while improving system throughput.

关键词： D2D communication resource allocation power control Multi-Agent Q-learning fuzzy C-means

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