A Cognitive Model for Multi-Agent Collaboration

In multi-agent system, agents work together for solving complex tasks and reaching common goals. In this paper, we propose a cognitive model for multi-agent collaboration. Based on the cognitive model, an agent architecture will also be presented. This agent has BDI, awareness and policy driven mechanism concurrently. These approaches are integrated in one agent that will make multi-agent collaboration more practical in the real world.

A multi-agent collaboration scheme for energy-efficient task scheduling in a 3D UAV-MEC space

Multi-access edge computing(MEC)presents computing services at the edge of networks to address the enormous processing requirements of intelligent applications.Due to the maneuverability of unmanned aerial vehicles(UAVs),they can be used as temporal aerial edge nodes for providing edge services to ground users in MEC.However,MEC environment is usually dynamic and complicated.It is a challenge for multiple UAVs to select appropriate service strategies.Besides,most of existing works study UAV-MEC with the assumption that the flight heights of UAVs are fixed;i.e.,the flying is considered to occur with reference to a two-dimensional plane,which neglects the importance of the height.In this paper,with consideration of the co-channel interference,an optimization problem of energy efficiency is investigated to maximize the number of fulfilled tasks,where multiple UAVs in a threedimensional space collaboratively fulfill the task computation of ground users.In the formulated problem,we try to obtain the optimal flight and sub-channel selection strategies for UAVs and schedule strategies for tasks.Based on the multi-agent deep deterministic policy gradient(MADDPG)algorithm,we propose a curiosity-driven and twin-networks-structured MADDPG(CTMADDPG)algorithm to solve the formulated problem.It uses the inner reward to facilitate the state exploration of agents,avoiding convergence at the sub-optimal strategy.Furthermore,we adopt the twin critic networks for update stabilization to reduce the probability of Q value overestimation.The simulation results show that CTMADDPG is outstanding in maximizing the energy efficiency of the whole system and outperforms the other benchmarks.

A Weighted Mean Field Reinforcement Learning Algorithm for Large-Scale Multi-Agent Collaboration

Reinforcement learning has been proven to be an effective approach for solving multi-agent coordination problems in a dynamic open environment.For dealing with multi-agent cooper-ation issues,the mean field multi-agent reinforcement leaming method can better overcome the problems of slow learning speed,unstable convergent performance,and poor learning effect.However,the original mean field algorithm cannot extract features well when agents cooperate.In order to solve the large-scale multi-agent coordination problem,in this paper,the mean field multi-agent reinforcement learning algorithm is improved and optimized by combining the multi-head attention mechanism,and the attention-based mean field(MFA)structure is designed.The employment of a multi-head attention mechanism can optimize the interaction among agents,extract more effective cluster features and enable agents to learn more efficient strategies.This paper first introduces the framework structure of MFA and then expounds on the relevant theoretical basis based on the Q-Learning and Actor-Critic algorithms,and finally conducts large-scale multi-agent cooperative experiments on the MAgent platform.The ex-perimental results show that compared with the baseline algorithm,the attention-based mean field Q-learning(MFQA)and attention-based Actor-Critic(MFACA)algorithms can make large-scale multi-agent clusters converge to higher rewards,and perform better than the original mean field multi-agent algorithm.

基于Multi-Agent的无人机集群体系自主作战系统设计

针对无人集群自主作战体系设计中的关键问题,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。仿真结果表明,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集群的协同作战效能,最后总结了集群协同作战的策略和经验。

基于Multi-Agent的水电站变压器故障诊断系统

为了精准、快速完成水电站变压器的故障诊断,设计基于Multi-Agent的水电站变压器故障诊断系统。变压器状态监控agent将检测到的变压器故障信息发送给系统管理agent,系统管理agent通过通信agent将变压器故障信息发送给变压器故障诊断agent,变压器故障诊断agent利用小波变换方法提取变压器故障特征,并将其作为IFOA-SVM模型输入,完成变压器故障分类后,获取变压器故障诊断结果,该结果通过通信agent显示给用户。实验表明,该系统可有效诊断变压器故障诊断,诊断成功率受系统故障信息丢失率的影响较小,诊断耗时、耗能小,并具有较高故障诊断成功率。

UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.

Robust and Trustworthy Data Sharing Framework Leveraging On-Chain and Off-Chain Collaboration

The proliferation of Internet of Things(IoT)systems has resulted in the generation of substantial data,presenting new challenges in reliable storage and trustworthy sharing.Conventional distributed storage systems are hindered by centralized management and lack traceability,while blockchain systems are limited by low capacity and high latency.To address these challenges,the present study investigates the reliable storage and trustworthy sharing of IoT data,and presents a novel system architecture that integrates on-chain and off-chain data manage systems.This architecture,integrating blockchain and distributed storage technologies,provides high-capacity,high-performance,traceable,and verifiable data storage and access.The on-chain system,built on Hyperledger Fabric,manages metadata,verification data,and permission information of the raw data.The off-chain system,implemented using IPFS Cluster,ensures the reliable storage and efficient access to massive files.A collaborative storage server is designed to integrate on-chain and off-chain operation interfaces,facilitating comprehensive data operations.We provide a unified access interface for user-friendly system interaction.Extensive testing validates the system’s reliability and stable performance.The proposed approach significantly enhances storage capacity compared to standalone blockchain systems.Rigorous reliability tests consistently yield positive outcomes.With average upload and download throughputs of roughly 20 and 30 MB/s,respectively,the system’s throughput surpasses the blockchain system by a factor of 4 to 18.

Discovering Latent Variables for the Tasks With Confounders in Multi-Agent Reinforcement Learning

Efficient exploration in complex coordination tasks has been considered a challenging problem in multi-agent reinforcement learning(MARL). It is significantly more difficult for those tasks with latent variables that agents cannot directly observe. However, most of the existing latent variable discovery methods lack a clear representation of latent variables and an effective evaluation of the influence of latent variables on the agent. In this paper, we propose a new MARL algorithm based on the soft actor-critic method for complex continuous control tasks with confounders. It is called the multi-agent soft actor-critic with latent variable(MASAC-LV) algorithm, which uses variational inference theory to infer the compact latent variables representation space from a large amount of offline experience.Besides, we derive the counterfactual policy whose input has no latent variables and quantify the difference between the actual policy and the counterfactual policy via a distance function. This quantified difference is considered an intrinsic motivation that gives additional rewards based on how much the latent variable affects each agent. The proposed algorithm is evaluated on two collaboration tasks with confounders, and the experimental results demonstrate the effectiveness of MASAC-LV compared to other baseline algorithms.

A survey on multi-agent reinforcement learning and its application

Multi-agent reinforcement learning(MARL)has been a rapidly evolving field.This paper presents a comprehensive survey of MARL and its applications.We trace the historical evolution of MARL,highlight its progress,and discuss related survey works.Then,we review the existing works addressing inherent challenges and those focusing on diverse applications.Some representative stochastic games,MARL means,spatial forms of MARL,and task classification are revisited.We then conduct an in-depth exploration of a variety of challenges encountered in MARL applications.We also address critical operational aspects,such as hyperparameter tuning and computational complexity,which are pivotal in practical implementations of MARL.Afterward,we make a thorough overview of the applications of MARL to intelligent machines and devices,chemical engineering,biotechnology,healthcare,and societal issues,which highlights the extensive potential and relevance of MARL within both current and future technological contexts.Our survey also encompasses a detailed examination of benchmark environments used in MARL research,which are instrumental in evaluating MARL algorithms and demonstrate the adaptability of MARL to diverse application scenarios.In the end,we give our prospect for MARL and discuss their related techniques and potential future applications.

Global collaboration of eye research--personal experience

On December 9,2023,I was privileged to be honored and participate in the Dr.Chi Chao Chan Symposium on Global Collaboration of Eye Research as the Global Eye Genetic Consortium(GEGC)session,which was held in the 16th Congress of the Asia-Pacific Vitreo-Retina Society(APVRS)in Hong Kong.Along with my talk on“Global collaboration of eye research:personal experience”,other prominent international speakers provided their own perspectives on opportunities for networking,collaboration,and exchange of ideas with global leaders and experts in ophthalmic practice,research,and education.

CoopAI-Route: DRL Empowered Multi-Agent Cooperative System for Efficient QoS-Aware Routing for Network Slicing in Multi-Domain SDN

The emergence of beyond 5G networks has the potential for seamless and intelligent connectivity on a global scale.Network slicing is crucial in delivering services for different,demanding vertical applications in this context.Next-generation applications have time-sensitive requirements and depend on the most efficient routing path to ensure packets reach their intended destinations.However,the existing IP(Internet Protocol)over a multi-domain network faces challenges in enforcing network slicing due to minimal collaboration and information sharing among network operators.Conventional inter-domain routing methods,like Border Gateway Protocol(BGP),cannot make routing decisions based on performance,which frequently results in traffic flowing across congested paths that are never optimal.To address these issues,we propose CoopAI-Route,a multi-agent cooperative deep reinforcement learning(DRL)system utilizing hierarchical software-defined networks(SDN).This framework enforces network slicing in multi-domain networks and cooperative communication with various administrators to find performance-based routes in intra-and inter-domain.CoopAI-Route employs the Distributed Global Topology(DGT)algorithm to define inter-domain Quality of Service(QoS)paths.CoopAI-Route uses a DRL agent with a message-passing multi-agent Twin-Delayed Deep Deterministic Policy Gradient method to ensure optimal end-to-end routes adapted to the specific requirements of network slicing applications.Our evaluation demonstrates CoopAI-Route’s commendable performance in scalability,link failure handling,and adaptability to evolving topologies compared to state-of-the-art methods.

Bipartite consensus problems of Lurie multi-agent systems over signed graphs: A contraction approach

This paper examines the bipartite consensus problems for the nonlinear multi-agent systems in Lurie dynamics form with cooperative and competitive communication between different agents. Based on the contraction theory, some new conditions for the nonlinear Lurie multi-agent systems reaching bipartite leaderless consensus and bipartite tracking consensus are presented. Compared with the traditional methods, this approach degrades the dimensions of the conditions, eliminates some restrictions of the system matrix, and extends the range of the nonlinear function. Finally, two numerical examples are provided to illustrate the efficiency of our results.

Hyperbolic Tangent Function-Based Protocols for Global/Semi-Global Finite-Time Consensus of Multi-Agent Systems

This paper investigates the problem of global/semi-global finite-time consensus for integrator-type multi-agent sys-tems.New hyperbolic tangent function-based protocols are pro-posed to achieve global and semi-global finite-time consensus for both single-integrator and double-integrator multi-agent systems with leaderless undirected and leader-following directed commu-nication topologies.These new protocols not only provide an explicit upper-bound estimate for the settling time,but also have a user-prescribed bounded control level.In addition,compared to some existing results based on the saturation function,the pro-posed approach considerably simplifies the protocol design and the stability analysis.Illustrative examples and an application demonstrate the effectiveness of the proposed protocols.

Designing Proportional-Integral Consensus Protocols for Second-Order Multi-Agent Systems Using Delayed and Memorized State Information

This paper is concerned with consensus of a secondorder linear time-invariant multi-agent system in the situation that there exists a communication delay among the agents in the network.A proportional-integral consensus protocol is designed by using delayed and memorized state information.Under the proportional-integral consensus protocol,the consensus problem of the multi-agent system is transformed into the problem of asymptotic stability of the corresponding linear time-invariant time-delay system.Note that the location of the eigenvalues of the corresponding characteristic function of the linear time-invariant time-delay system not only determines the stability of the system,but also plays a critical role in the dynamic performance of the system.In this paper,based on recent results on the distribution of roots of quasi-polynomials,several necessary conditions for Hurwitz stability for a class of quasi-polynomials are first derived.Then allowable regions of consensus protocol parameters are estimated.Some necessary and sufficient conditions for determining effective protocol parameters are provided.The designed protocol can achieve consensus and improve the dynamic performance of the second-order multi-agent system.Moreover,the effects of delays on consensus of systems of harmonic oscillators/double integrators under proportional-integral consensus protocols are investigated.Furthermore,some results on proportional-integral consensus are derived for a class of high-order linear time-invariant multi-agent systems.

Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information Systems

In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic Trading.Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading strategies.Through meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions.These AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital landscape.Our research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital age.Additionally,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.

Targeted multi-agent communication algorithm based on state control

As an important mechanism in multi-agent interaction,communication can make agents form complex team relationships rather than constitute a simple set of multiple independent agents.However,the existing communication schemes can bring much timing redundancy and irrelevant messages,which seriously affects their practical application.To solve this problem,this paper proposes a targeted multiagent communication algorithm based on state control(SCTC).The SCTC uses a gating mechanism based on state control to reduce the timing redundancy of communication between agents and determines the interaction relationship between agents and the importance weight of a communication message through a series connection of hard-and self-attention mechanisms,realizing targeted communication message processing.In addition,by minimizing the difference between the fusion message generated from a real communication message of each agent and a fusion message generated from the buffered message,the correctness of the final action choice of the agent is ensured.Our evaluation using a challenging set of Star Craft II benchmarks indicates that the SCTC can significantly improve the learning performance and reduce the communication overhead between agents,thus ensuring better cooperation between agents.

Safety-Constrained Multi-Agent Reinforcement Learning for Power Quality Control in Distributed Renewable Energy Networks

This paper examines the difficulties of managing distributed power systems,notably due to the increasing use of renewable energy sources,and focuses on voltage control challenges exacerbated by their variable nature in modern power grids.To tackle the unique challenges of voltage control in distributed renewable energy networks,researchers are increasingly turning towards multi-agent reinforcement learning(MARL).However,MARL raises safety concerns due to the unpredictability in agent actions during their exploration phase.This unpredictability can lead to unsafe control measures.To mitigate these safety concerns in MARL-based voltage control,our study introduces a novel approach:Safety-ConstrainedMulti-Agent Reinforcement Learning(SC-MARL).This approach incorporates a specialized safety constraint module specifically designed for voltage control within the MARL framework.This module ensures that the MARL agents carry out voltage control actions safely.The experiments demonstrate that,in the 33-buses,141-buses,and 322-buses power systems,employing SC-MARL for voltage control resulted in a reduction of the Voltage Out of Control Rate(%V.out)from0.43,0.24,and 2.95 to 0,0.01,and 0.03,respectively.Additionally,the Reactive Power Loss(Q loss)decreased from 0.095,0.547,and 0.017 to 0.062,0.452,and 0.016 in the corresponding systems.

Finite-time Prescribed Performance Time-Varying Formation Control for Second-Order Multi-Agent Systems With Non-Strict Feedback Based on a Neural Network Observer

This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities.To eliminate nonlinearities,neural networks are applied to approximate the inherent dynamics of the system.In addition,due to the limitations of the actual working conditions,each follower agent can only obtain the locally measurable partial state information of the leader agent.To address this problem,a neural network state observer based on the leader state information is designed.Then,a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region,which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time.Finally,a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.

A coordinated operation method of wind-PV-hydrogenstorage multi-agent energy system

Wind-photovoltaic(PV)-hydrogen-storage multi-agent energy systems are expected to play an important role in promoting renewable power utilization and decarbonization.In this study,a coordinated operation method was proposed for a wind-PVhydrogen-storage multi-agent energy system.First,a coordinated operation model was formulated for each agent considering peer-to-peer power trading.Second,a coordinated operation interactive framework for a multi-agent energy system was proposed based on the theory of the alternating direction method of multipliers.Third,a distributed interactive algorithm was proposed to protect the privacy of each agent and solve coordinated operation strategies.Finally,the effectiveness of the proposed coordinated operation method was tested on multi-agent energy systems with different structures,and the operational revenues of the wind power,PV,hydrogen,and energy storage agents of the proposed coordinated operation model were improved by approximately 59.19%,233.28%,16.75%,and 145.56%,respectively,compared with the independent operation model.

Service Function Chain Deployment Algorithm Based on Multi-Agent Deep Reinforcement Learning

Aiming at the rapid growth of network services,which leads to the problems of long service request processing time and high deployment cost in the deployment of network function virtualization service function chain(SFC)under 5G networks,this paper proposes a multi-agent deep deterministic policy gradient optimization algorithm for SFC deployment(MADDPG-SD).Initially,an optimization model is devised to enhance the request acceptance rate,minimizing the latency and deploying the cost SFC is constructed for the network resource-constrained case.Subsequently,we model the dynamic problem as a Markov decision process(MDP),facilitating adaptation to the evolving states of network resources.Finally,by allocating SFCs to different agents and adopting a collaborative deployment strategy,each agent aims to maximize the request acceptance rate or minimize latency and costs.These agents learn strategies from historical data of virtual network functions in SFCs to guide server node selection,and achieve approximately optimal SFC deployment strategies through a cooperative framework of centralized training and distributed execution.Experimental simulation results indicate that the proposed method,while simultaneously meeting performance requirements and resource capacity constraints,has effectively increased the acceptance rate of requests compared to the comparative algorithms,reducing the end-to-end latency by 4.942%and the deployment cost by 8.045%.