In this paper we tackle the problem of routing multiple agents in a coordinated manner. This is a complex problem that has a wide range of applications in fleet management to achieve a common goal, such as mapping from a swarm of robots and ride sharing. Traditional methods are typically not designed for realistic environments hich contain sparsely connected graphs and unknown traffic, and are often too slow in runtime to be practical. In contrast, we propose a graph neural network based model that is able to perform multi-agent routing based on learned value iteration in a sparsely connected graph with dynamically changing traffic conditions. Moreover, our learned communication module enables the agents to coordinate online and adapt to changes more effectively. We created a simulated environment to mimic realistic mapping performed by autonomous vehicles with unknown minimum edge coverage and traffic conditions; our approach significantly outperforms traditional solvers both in terms of total cost and runtime. We also show that our model trained with only two agents on graphs with a maximum of 25 nodes can easily generalize to situations with more agents and/or nodes.
Traffic congestion in urban road networks leads to longer trip times and higher emissions, especially during peak periods. While the Shortest Path First (SPF) algorithm is optimal for a single vehicle in a static network, it performs poorly in dynamic, multi-vehicle settings, often worsening congestion by routing all vehicles along identical paths. We address dynamic vehicle routing through a multi-agent reinforcement learning (MARL) framework for coordinated, network-aware fleet navigation. We first propose Adaptive Navigation (AN), a decentralized MARL model where each intersection agent pro…
Modality-Native Routing in Agent-to-Agent Networks: A Multimodal A2A Protocol Extension
AI Architect, Author—Data Engineering for Multimodal AI (O’Reilly)（人工智能架构师，作者—多模态AI的数据工程（O’Reilly））；Stanford School of Engineering (April 2026)（斯坦福大学工程学院（2026年4月））
In task allocation for real-time domains, such as disaster response, a limited number of agents is deployed across a large area to carry out numerous tasks, each with its prerequisites, profit, time window and workload. To maximize profits while minimizing time penalties, agents need to cooperate by forming, disbanding and reforming coalitions. In this paper, we name this problem Multi-Agent Routing and Scheduling through Coalition formation (MARSC) and show that it generalizes the important Team Orienteering Problem with Time Windows. We propose a binary integer program and an anytime and sca…
In cooperative multi-agent reinforcement learning (MARL), how to design a suitable reward signal to accelerate learning and stabilize convergence is a critical problem. The global reward signal assigns the same global reward to all agents without distinguishing their contributions, while the local reward signal provides different local rewards to each agent based solely on individual behavior. Both of the two reward assignment approaches have some shortcomings: the former might encourage lazy agents, while the latter might produce selfish agents. In this paper, we study reward design problem i…
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Research Institue of Big Data（深圳大数据研究院）
Packet routing is a fundamental problem in communication networks that decides how the packets are directed from their source nodes to their destination nodes through some intermediate nodes. With the increasing complexity of network topology and highly dynamic traffic demand, conventional model-based and rule-based routing schemes show significant limitations, due to the simplified and unrealistic model assumptions, and lack of flexibility and adaption. Adding intelligence to the network control is becoming a trend and the key to achieving high-efficiency network operation. In this paper, we …
Efficient and Interpretable Multi-Agent LLM Routing via Ant Colony Optimization
School of Computing, Kyung Hee University（Kyung Hee 大学计算机学院）；School of Information and Software Engineering, University of Electronic Science and Technology of China（中国电子科技大学信息与软件工程学院）；School of Computer Science and Engineering, University of Electronic Science and Technology of China（中国电子科技大学计算机科学与工程学院）；School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）
Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning
Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi（印度理工学院德里分校雅迪人工智能学院）；Department of Electrical Engineering, Indian Institute of Technology Delhi（印度理工学院德里分校电气工程系）
Multi-vehicle routing problem with soft time windows (MVRPSTW) is an indispensable constituent in urban logistics distribution systems. Over the past decade, numerous methods for MVRPSTW have been proposed, but most are based on heuristic rules that require a large amount of computation time. With the current rapid increase of logistics demands, traditional methods incur the dilemma between computational efficiency and solution quality. To efficiently solve the problem, we propose a novel reinforcement learning algorithm called the Multi-Agent Attention Model that can solve routing problem ins…
EpiSys Science, Inc.（EpiSys科学公司）；Boeing Research and Technology（波音研究与技术）
Highly dynamic mobile ad-hoc networks (MANETs) remain as one of the most challenging environments to develop and deploy robust, efficient, and scalable routing protocols. In this paper, we present DeepCQ+ routing protocol which, in a novel manner integrates emerging multi-agent deep reinforcement learning (MADRL) techniques into existing Q-learning-based routing protocols and their variants and achieves persistently higher performance across a wide range of topology and mobility configurations. While keeping the overall protocol structure of the Q-learning-based routing protocols, DeepCQ+ repl…
Fast moving but power hungry unmanned aerial vehicles (UAVs) can recharge on slow-moving unmanned ground vehicles (UGVs) to survey large areas in an effective and efficient manner. In order to solve this computationally challenging problem in a reasonable time, we created a two-level optimization heuristics. At the outer level, the UGV route is parameterized by few free parameters and at the inner level, the UAV route is solved by formulating and solving a vehicle routing problem with capacity constraints, time windows, and dropped visits. The UGV free parameters need to be optimized judicious…
DyTopo: Dynamic Topology Routing for Multi-Agent Reasoning via Semantic Matching
Peking University, Beijing, China（北京大学）；Georgia Institute of Technology, Atlanta, United States（佐治亚理工学院）；Southeast University, Location, Country（东南大学）；Tsinghua University（清华大学）
Orchestrating Intelligence: Confidence-Aware Routing for Efficient Multi-Agent Collaboration across Multi-Scale Models
Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, China（社会科学与信息检索研究中心，哈尔滨工业大学，中国）；the Institute of Automation of the Chinese Academy of Sciences, China（中国科学院自动化研究所，中国）
CASTER: Breaking the Cost-Performance Barrier in Multi-Agent Orchestration via Context-Aware Strategy for Task Efficient Routing
Qingdao Institute of Software, College of Computer Science；Technology, China University of Petroleum (East China)；Shandong Key Laboratory of Intelligent Oil \& Gas Industrial Software；School of computing；artificial intelligence, Southwest Jiaotong University；Computer Engineering Department, University of Houston
We consider the problem of designing the the utility functions of the utility-maximizing agents in a multi-agent system so that they work synergistically to maximize a global utility. The particular problem domain we explore is the control of network routing by placing agents on all the routers in the network. Conventional approaches to this task have the agents all use the Ideal Shortest Path routing Algorithm (ISPA). We demonstrate that in many cases, due to the side-effects of one agent's actions on another agent's performance, having agents use ISPA's is suboptimal as far as global aggrega…
New York University（纽约大学）；Siemens Corporation（西门子公司）；Shanghai Jiao Tong University（上海交通大学）
Emergency vehicles (EMVs) play a crucial role in responding to time-critical calls such as medical emergencies and fire outbreaks in urban areas. Existing methods for EMV dispatch typically optimize routes based on historical traffic-flow data and design traffic signal pre-emption accordingly; however, we still lack a systematic methodology to address the coupling between EMV routing and traffic signal control. In this paper, we propose EMVLight, a decentralized reinforcement learning (RL) framework for joint dynamic EMV routing and traffic signal pre-emption. We adopt the multi-agent advantag…
An Overlay Multicast Routing Method Based on Network Situational Awareness and Hierarchical Multi-Agent Reinforcement Learning
School of Information and Communication, Guilin University of Electronic Technology（桂林电子科技大学信息与通信学院）；School of Computer Science and Information Security, Guilin University of Electronic Technology（桂林电子科技大学计算机科学与信息安全学院）；Information Center, Guilin Medical University（桂林医学院信息中心）；School of Optoelectronic Engineering, Guilin University of Electronic Technology（桂林电子科技大学光电工程学院）
Monash University（莫纳什大学）；University of Southern California（南加州大学）
The 2D Multi-Agent Path Finding (MAPF) problem aims at finding collision-free paths for a number of agents, from a set of start locations to a set of goal positions in a known 2D environment. MAPF has been studied in theoretical computer science, robotics, and artificial intelligence over several decades, due to its importance for robot navigation. It is currently experiencing significant scientific progress due to its relevance in automated warehousing (such as those operated by Amazon) and in other contemporary application areas. In this paper, we demonstrate that many recently developed MAP…
Federal University of Minas Gerais（米纳斯吉拉斯联邦大学）；University of Campinas（坎皮纳斯大学）
The worldwide growth of e-commerce has created new challenges for logistics companies, one of which is being able to deliver products quickly and at low cost, which reflects directly in the way of sorting packages, needing to eliminate steps such as storage and batch creation. Our work presents a multi-agent system that uses trajectory data mining techniques to extract territorial patterns and use them in the dynamic creation of last-mile routes. The problem can be modeled as a Dynamic Capacitated Vehicle Routing Problem (VRP) with Stochastic Customer, being therefore NP-HARD, what makes its i…
AgentGate: A Lightweight Structured Routing Engine for the Internet of Agents
School of Artificial Intelligence, University of Science and Technology Beijing（北京科技大学人工智能学院）；China Telecom Research Institute（中国电信研究院）；Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（中国科学院大学杭州高等研究院）
University of Texas at Austin（德克萨斯大学奥斯汀分校）；Duke University（杜克大学）；Cornell University（康奈尔大学）
This paper focuses on two commonly used path assignment policies for agents traversing a congested network: self-interested routing, and system-optimum routing. In the self-interested routing policy each agent selects a path that optimizes its own utility, while the system-optimum routing agents are assigned paths with the goal of maximizing system performance. This paper considers a scenario where a centralized network manager wishes to optimize utilities over all agents, i.e., implement a system-optimum routing policy. In many real-life scenarios, however, the system manager is unable to inf…
EpiSys Science, Inc.（EpiSys科学公司）；Boeing Research and Technology（波音研究与技术公司）
Highly dynamic mobile ad-hoc networks (MANETs) are continuing to serve as one of the most challenging environments to develop and deploy robust, efficient, and scalable routing protocols. In this paper, we present DeepCQ+ routing which, in a novel manner, integrates emerging multi-agent deep reinforcement learning (MADRL) techniques into existing Q-learning-based routing protocols and their variants, and achieves persistently higher performance across a wide range of MANET configurations while training only on a limited range of network parameters and conditions. Quantitatively, DeepCQ+ shows …
Fudan University（复旦大学）；East China University of Science and Technology（华东理工大学）；Hubei Normal University（湖北师范大学）
Packet routing is one of the fundamental problems in computer networks in which a router determines the next-hop of each packet in the queue to get it as quickly as possible to its destination. Reinforcement learning (RL) has been introduced to design autonomous packet routing policies with local information of stochastic packet arrival and service. However, the curse of dimensionality of RL prohibits the more comprehensive representation of dynamic network states, thus limiting its potential benefit. In this paper, we propose a novel packet routing framework based on \emph{multi-agent} deep r…
