Scalable Multi-Agent Path Finding using Collision-Aware Dynamic Alert Mask and a Hybrid Execution Strategy

• URL: http://arxiv.org/abs/2510.09469v1
• Date: Fri, 10 Oct 2025 15:25:40 GMT
• Title: Scalable Multi-Agent Path Finding using Collision-Aware Dynamic Alert Mask and a Hybrid Execution Strategy
• Authors: Bharath Muppasani, Ritirupa Dey, Biplav Srivastava, Vignesh Narayanan,
• Abstract summary: Multi-agent pathfinding is a critical problem in robotics and autonomous systems.<n>We propose a hybrid framework that combines decentralized path planning with a lightweight centralized coordinator.
• Score: 14.091856940824258
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-agent pathfinding (MAPF) remains a critical problem in robotics and autonomous systems, where agents must navigate shared spaces efficiently while avoiding conflicts. Traditional centralized algorithms that have global information, such as Conflict-Based Search (CBS), provide high-quality solutions but become computationally expensive in large-scale scenarios due to the combinatorial explosion of conflicts that need resolution. Conversely, distributed approaches that have local information, particularly learning-based methods, offer better scalability by operating with relaxed information availability, yet often at the cost of solution quality. To address these limitations, we propose a hybrid framework that combines decentralized path planning with a lightweight centralized coordinator. Our framework leverages reinforcement learning (RL) for decentralized planning, enabling agents to adapt their planning based on minimal, targeted alerts--such as static conflict-cell flags or brief conflict tracks--that are dynamically shared information from the central coordinator for effective conflict resolution. We empirically study the effect of the information available to an agent on its planning performance. Our approach reduces the inter-agent information sharing compared to fully centralized and distributed methods, while still consistently finding feasible, collision-free solutions--even in large-scale scenarios having higher agent counts.

Related papers

• MO-MIX: Multi-Objective Multi-Agent Cooperative Decision-Making With Deep Reinforcement Learning [68.91090643731987]
  Deep reinforcement learning (RL) has been applied extensively to solve complex decision-making problems.<n>Existing approaches are limited to separate fields and can only handle multi-agent decision-making with a single objective.<n>We propose MO-mix to solve the multi-objective multi-agent reinforcement learning (MOMARL) problem.
  arXiv  Detail & Related papers  (2026-02-28T16:25:22Z)
• Multi-Agent Deep Reinforcement Learning for Collaborative UAV Relay Networks under Jamming Atatcks [36.380478794869234]
  This paper formulates this challenge as a cooperative Multi-Agent Reinforcement Learning (MARL) problem, solved using the Training with Decentralized Execution (CTDE) framework.<n>Our proposed framework significantly outperforms baselines, increasing total system throughput by approximately 50% while simultaneously achieving a near-zero collision rate.<n>A key finding is that the agents develop an emergent anti-jamming strategy without explicit programming.
  arXiv  Detail & Related papers  (2025-12-09T08:11:21Z)
• Multi-Agent Reinforcement Learning for Task Offloading in Wireless Edge Networks [3.065057842316041]
  In edge computing systems, autonomous agents must make fast local decisions while competing for shared resources.<n>We propose a decentralized framework in which each agent solves a constrained Markov decision process (CMDP), coordinating implicitly through a shared constraint vector.<n>Using safe reinforcement learning, agents learn policies that meet both local and global goals.
  arXiv  Detail & Related papers  (2025-09-01T08:47:36Z)
• Cluster-Based Multi-Agent Task Scheduling for Space-Air-Ground Integrated Networks [60.085771314013044]
  Low-altitude economy holds significant potential for development in areas such as communication and sensing.<n>We propose a Clustering-based Multi-agent Deep Deterministic Policy Gradient (CMADDPG) algorithm to address the multi-UAV cooperative task scheduling challenges in SAGIN.
  arXiv  Detail & Related papers  (2024-12-14T06:17:33Z)
• Generalizability of Graph Neural Networks for Decentralized Unlabeled Motion Planning [72.86540018081531]
  Unlabeled motion planning involves assigning a set of robots to target locations while ensuring collision avoidance. This problem forms an essential building block for multi-robot systems in applications such as exploration, surveillance, and transportation. We address this problem in a decentralized setting where each robot knows only the positions of its $k$-nearest robots and $k$-nearest targets.
  arXiv  Detail & Related papers  (2024-09-29T23:57:25Z)
• Design Optimization of NOMA Aided Multi-STAR-RIS for Indoor Environments: A Convex Approximation Imitated Reinforcement Learning Approach [51.63921041249406]
  Non-orthogonal multiple access (NOMA) enables multiple users to share the same frequency band, and simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) deploying STAR-RIS indoors presents challenges in interference mitigation, power consumption, and real-time configuration. A novel network architecture utilizing multiple access points (APs), STAR-RISs, and NOMA is proposed for indoor communication.
  arXiv  Detail & Related papers  (2024-06-19T07:17:04Z)
• DePAint: A Decentralized Safe Multi-Agent Reinforcement Learning Algorithm considering Peak and Average Constraints [1.1549572298362787]
  We propose a momentum-based decentralized gradient policy method, DePAint, to solve the problem. This is the first privacy-preserving fully decentralized multi-agent reinforcement learning algorithm that considers both peak and average constraints.
  arXiv  Detail & Related papers  (2023-10-22T16:36:03Z)
• Learn to Follow: Decentralized Lifelong Multi-agent Pathfinding via Planning and Learning [46.354187895184154]
  Multi-agent Pathfinding (MAPF) problem generally asks to find a set of conflict-free paths for a set of agents confined to a graph. In this work, we investigate the decentralized MAPF setting, when the central controller that posses all the information on the agents' locations and goals is absent. We focus on the practically important lifelong variant of MAPF, which involves continuously assigning new goals to the agents upon arrival to the previous ones.
  arXiv  Detail & Related papers  (2023-10-02T13:51:32Z)
• Multi-Agent Path Finding with Prioritized Communication Learning [44.89255851944412]
  We propose a PrIoritized COmmunication learning method (PICO), which incorporates the textitimplicit planning priorities into the communication topology. PICO performs significantly better in large-scale MAPF tasks in success rates and collision rates than state-of-the-art learning-based planners.
  arXiv  Detail & Related papers  (2022-02-08T04:04:19Z)
• F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning [110.35516334788687]
  Decentralized multi-agent reinforcement learning algorithms are sometimes unpractical in complicated applications. We propose a flexible fully decentralized actor-critic MARL framework, which can handle large-scale general cooperative multi-agent setting. Our framework can achieve scalability and stability for large-scale environment and reduce information transmission.
  arXiv  Detail & Related papers  (2020-04-17T14:56:29Z)
• Decentralized MCTS via Learned Teammate Models [89.24858306636816]
  We present a trainable online decentralized planning algorithm based on decentralized Monte Carlo Tree Search. We show that deep learning and convolutional neural networks can be employed to produce accurate policy approximators.
  arXiv  Detail & Related papers  (2020-03-19T13:10:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.