# GRAND: Guidance, Rebalancing, and Assignment for Networked Dispatch in Multi-Agent Path Finding

arXiv: https://arxiv.org/abs/2512.03194 · HTML: https://mlsanity.com/paper/2512.03194/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 33 references that are in the map.
- First posted on arXiv 2 Dec 2025.

## Builds on (most cited first, 19 of 19)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification](https://mlsanity.com/paper/2009.03509/) (2020)
- [Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks](https://mlsanity.com/paper/1705.10868/) (2017)
- [Integrated Task Assignment and Path Planning for Capacitated Multi-Agent Pickup and Delivery](https://mlsanity.com/paper/2110.14891/) (2021)
- [Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems](https://mlsanity.com/paper/2104.11434/) (2021)
- [Traffic Flow Optimisation for Lifelong Multi-Agent Path Finding](https://mlsanity.com/paper/2308.11234/) (2023)
- [Idle Time Optimization for Target Assignment and Path Finding in Sortation Centers](https://mlsanity.com/paper/1912.00253/) (2019)
- [Multi-Goal Multi-Agent Pickup and Delivery](https://mlsanity.com/paper/2208.01223/) (2022)
- [RTAW: An Attention Inspired Reinforcement Learning Method for Multi-Robot Task Allocation in Warehouse Environments](https://mlsanity.com/paper/2209.05738/) (2022)
- [Scaling Lifelong Multi-Agent Path Finding to More Realistic Settings: Research Challenges and Opportunities](https://mlsanity.com/paper/2404.16162/) (2024)
- [Graph Reinforcement Learning for Network Control via Bi-Level Optimization](https://mlsanity.com/paper/2305.09129/) (2023)
- [Analysis and Control of Autonomous Mobility-on-Demand Systems](https://mlsanity.com/paper/2106.14827/) (2021)
- [Online Guidance Graph Optimization for Lifelong Multi-Agent Path Finding](https://mlsanity.com/paper/2411.16506/) (2024)
- [Online Multi-Agent Pickup and Delivery with Task Deadlines](https://mlsanity.com/paper/2403.12377/) (2024)
- [Enhancing PIBT via Multi-Action Operations](https://mlsanity.com/paper/2511.09193/) (2025)
- [Robo-taxi Fleet Coordination at Scale via Reinforcement Learning](https://mlsanity.com/paper/2504.06125/) (2025)
- [Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding](https://mlsanity.com/paper/2505.19219/) (2025)
- [Parameterized Complexity of Vehicle Routing](https://mlsanity.com/paper/2509.10361/) (2025)
- [FICO: Finite-Horizon Closed-Loop Factorization for Unified Multi-Agent Path Finding](https://mlsanity.com/paper/2511.13961/) (2025)

## Abstract (arXiv)

Large robot fleets are now common in warehouses and other logistics settings, where small control gains translate into large operational impacts. In this article, we address task scheduling for lifelong Multi-Agent Pickup-and-Delivery (MAPD) and propose a hybrid method that couples learning-based global guidance with lightweight optimization. A graph neural network policy trained via reinforcement learning outputs a desired distribution of free agents over an aggregated warehouse graph. This signal is converted into region-to-region rebalancing through a minimum-cost flow, and finalized by small, local assignment problems, preserving accuracy while keeping per-step latency within a 1 s compute budget. We call this approach GRAND: a hierarchical algorithm that relies on Guidance, Rebalancing, and Assignment to explicitly leverage the workspace Network structure and Dispatch agents to tasks. On congested warehouse benchmarks from the League of Robot Runners (LoRR) with up to 500 agents, our approach improves throughput by up to 10% over the 2024 winning scheduler while maintaining real-time execution. The results indicate that coupling graph-structured learned guidance with tractable solvers reduces congestion and yields a practical, scalable blueprint for high-throughput scheduling in large fleets.