Multi-agent Approach Research Articles

UAV Swarm Cooperative Target Search: A Multi-Agent Reinforcement Learning Approach

UAV Swarm Cooperative Target Search: A Multi-Agent Reinforcement Learning Approach

A Multi-Agent System Approach for Balance Disorder Treatment: Integrating Computer Vision and Gamification

A Multi-Agent System Approach for Balance Disorder Treatment: Integrating Computer Vision and Gamification

Enhancing Cyber-Resilience in Electric Vehicle Charging Stations: A Multi-Agent Deep Reinforcement Learning Approach

Enhancing Cyber-Resilience in Electric Vehicle Charging Stations: A Multi-Agent Deep Reinforcement Learning Approach

Ratio-Based Offloading Optimization for Edge and Vehicular-Fog Federated Systems: A Multi-Agent TD3 Approach

Ratio-Based Offloading Optimization for Edge and Vehicular-Fog Federated Systems: A Multi-Agent TD3 Approach

Hierarchical Coordination Control for Interconnected Operation of Electric-thermal-gas Integrated Energy System with Micro-energy Internet Clusters

At present, the natural gas network, thermal network and power grid can be coupled in the integrated energy system(IES) to provide users with different forms of energy such as heat, electricity and gas, but the relevant coordinated control strategies have not been sufficiently investigated. In this paper, a hierarchical distributed coordinated control strategy is proposed. The system is organized into three levels, namely IES-level, Micro-energy Internet(MEI)-level and component-level based on multi-agent system approach. To achieve accurate control of voltage and frequency of distributed devices and accurate distribution of the tie-line power in case of load fluctuations in MEI, a second-level optimization controller based on distributed sparse communication network is designed. In addition, in order to analyze the impact of communication delay on the coordinated control of MEI-level agents, the communication topology is optimized according to the basic graph theory. Finally, taking IES with three MEIs as an example, the validity of the proposed control strategy is verified on the PSCAD/EMTDC simulation platform.

Optimal Time-of-use Pricing for Renewable Energy-powered Microgrids: A Multi-agent Evolutionary Game Theory-based Approach

Optimal Time-of-use Pricing for Renewable Energy-powered Microgrids: A Multi-agent Evolutionary Game Theory-based Approach

Two-Timescale Synchronization and Migration for Digital Twin Networks: A Multi-Agent Deep Reinforcement Learning Approach

Two-Timescale Synchronization and Migration for Digital Twin Networks: A Multi-Agent Deep Reinforcement Learning Approach

Evolutionary optimization for risk-aware heterogeneous multi-agent path planning in uncertain environments.

Cooperative multi-agent systems make it possible to employ miniature robots in order to perform different experiments for data collection in wide open areas to physical interactions with test subjects in confined environments such as a hive. This paper proposes a new multi-agent path-planning approach to determine a set of trajectories where the agents do not collide with each other or any obstacle. The proposed algorithm leverages a risk-aware probabilistic roadmap algorithm to generate a map, employs node classification to delineate exploration regions, and incorporates a customized genetic framework to address the combinatorial optimization, with the ultimate goal of computing safe trajectories for the team. Furthermore, the proposed planning algorithm makes the agents explore all subdomains in the workspace together as a formation to allow the team to perform different tasks or collect multiple datasets for reliable localization or hazard detection. The objective function for minimization includes two major parts, the traveling distance of all the agents in the entire mission and the probability of collisions between the agents or agents with obstacles. A sampling method is used to determine the objective function considering the agents' dynamic behavior influenced by environmental disturbances and uncertainties. The algorithm's performance is evaluated for different group sizes by using a simulation environment, and two different benchmark scenarios are introduced to compare the exploration behavior. The proposed optimization method establishes stable and convergent properties regardless of the group size.

Advancing Security and Trust in WSNs: A Federated Multi-Agent Deep Reinforcement Learning Approach

Advancing Security and Trust in WSNs: A Federated Multi-Agent Deep Reinforcement Learning Approach

Spectrum Efficient Mode Selection and Resource Allocation Optimization for D2D Communication in HetNet: A Multi-Agent Q-Learning Approach

Spectrum Efficient Mode Selection and Resource Allocation Optimization for D2D Communication in HetNet: A Multi-Agent Q-Learning Approach

Heuristic-Based Multi-Agent Deep Reinforcement Learning Approach for Coordinating Connected and Automated Vehicles at Non-Signalized Intersection

Heuristic-Based Multi-Agent Deep Reinforcement Learning Approach for Coordinating Connected and Automated Vehicles at Non-Signalized Intersection

Multi-Agent Systems and Foundation Models Enable Autonomous Supply Chains: Opportunities and Challenges

The demand for resilient and flexible supply chains has become even more apparent in the face of disruptions such as the COVID-pandemic and the ongoing Russia-Ukraine War. Whilst multi-agent system approaches have been proposed to increase resilience in supply chains for more than two decades, their development remains limited due to their difficulty of implementation and black box nature. The emergence of modern AI approaches including foundation models, enables the creation of generalist agents with multi-faceted decision-making capabilities. This opens up opportunities to create supply chain systems with self-orchestrating capabilities and heightened resilience, in a way that is human understandable, through natural language text. However, unlike areas such as healthcare and finance, the application of modern agent technology in the supply chain domain is under explored. This paper thus aims to conceptually explore this less studied domain, investigating the convergence between foundation models, multi-agent systems, and supply chain management. We discuss the opportunities and challenges arising from this convergence for enabling autonomous supply chains, and propose key future research topics to advance this convergence.

Reliable Low-Latency Routing for VLEO Satellite Optical Network: A Multi-agent Reinforcement Learning Approach

Reliable Low-Latency Routing for VLEO Satellite Optical Network: A Multi-agent Reinforcement Learning Approach

Joint Trajectory Control, Frequency Allocation, and Routing for UAV Swarm Networks: A Multi-Agent Deep Reinforcement Learning Approach

Joint Trajectory Control, Frequency Allocation, and Routing for UAV Swarm Networks: A Multi-Agent Deep Reinforcement Learning Approach

Multi-Agent Reinforcement Learning-Based Maximum Power Point Tracking Approach to Fortify PMSG-Based WECSs

Multi-Agent Reinforcement Learning-Based Maximum Power Point Tracking Approach to Fortify PMSG-Based WECSs

Towards Cooperative Driving among Heterogeneous CAVs: A Safe Multi-Agent Reinforcement Learning Approach

Towards Cooperative Driving among Heterogeneous CAVs: A Safe Multi-Agent Reinforcement Learning Approach

Simulation Modeling of Information Transfer Between Stock Exchanges Based on a Multi-Agent Model

The article substantiates the relevance of solving the problem of stock exchange modeling as an information system accumulating the orders of market participants and executing them. The object of the study is the process of information transfer between stock exchanges. The subject of the study is the use of simulation multi-agent model to study the process of information transfer between stock exchanges. The paper analyzes the possibilities of building a simulation model to analyze rare phenomena in the financial market using a multiagent approach. It is substantiated that this approach allows taking into account those features of complex dynamic systems, for which either no effective analytical approaches have been developed or it is impossible to conduct the necessary numerical experiments. The proposed logic of the market simulator is based on the allocation of certain trader classes, that may differ in the logic of making their decisions on the stock exchange, for this purpose the scheme of agent interaction in the model of the stock exchange is described. On the example of modeling the information transmission and market shocks between markets the possibilities of the proposed simulator are studied. The use of the proposed model in practice allows us to analyze rare phenomena occurring on the stock exchange, which cannot be described analytically, but their recreation by means of the simulation model will allow us to study them. The study of rare phenomena on the stock exchange with the help of simulation modeling allows to obtain different variants of the results of the phenomena at different sets of parameters of the model (agents), i.e. to carry out parameter.

Regional route guidance with realistic compliance patterns: Application of deep reinforcement learning and MPC

Solving link-based route guidance problems for large-scale networks is computationally challenging and faces practical issues, such as spatial–temporal data coverage. Thus, regional route guidance has emerged as a promising strategy, which utilizes regional approaches (i.e., network-level macroscopic traffic models) to capture traffic dynamics. Existing regional route guidance models have mainly focused on macroscopic flows and aggregated splitting rates, employing uniformly sampled vehicles as controllable targets. These models, however, overlook the inherent nature of individual drivers’ compliance. It may deteriorate the guidance performance as existing route guidance models cannot effectively generate customized route plans for compliant vehicles. This paper aims to introduce a regional route guidance framework with the utilization of different solution approaches, i.e. model-based and data-driven, considering the compliance pattern. In particular, MPC-based and deep reinforcement learning-based schemes are proposed for the information service provider to send customized route plans to compliant individuals. Within each solution approach, different route guidance strategies are developed for specific purposes and evaluated through numerical experiments under low- and high-congestion scenarios. Besides, the trade-off between total travel time and average trip length is studied, wherein the balance reward in a deep reinforcement learning-based approach enables controllable agents to reduce trip costs while compromising partial system utility. The findings indicate the effectiveness of the proposed strategies in alleviating network congestion. Additionally, multi-agent approaches outperform single-agent ones, highlighting the benefits of cooperative decision-making in traffic management.

Multi-Level Objective Control of AVs at a Saturated Signalized Intersection with Multi-Agent Deep Reinforcement Learning Approach

Multi-Level Objective Control of AVs at a Saturated Signalized Intersection with Multi-Agent Deep Reinforcement Learning Approach

Resource Allocation in Uplink NOMA Systems: A Hybrid-Decision-Based Multi-Agent Deep Reinforcement Learning Approach

Resource Allocation in Uplink NOMA Systems: A Hybrid-Decision-Based Multi-Agent Deep Reinforcement Learning Approach