# Multi-Agent Routing Value Iteration Network

arXiv: https://arxiv.org/abs/2007.05096 · HTML: https://mlsanity.com/paper/2007.05096/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2020.

## Built on by (most cited first, 17 of 17)

- [Matrix Encoding Networks for Neural Combinatorial Optimization](https://mlsanity.com/paper/2106.11113/) (2021)
- [Learning to Solve Vehicle Routing Problems: A Survey](https://mlsanity.com/paper/2205.02453/) (2022)
- [A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with Drone](https://mlsanity.com/paper/2112.12545/) (2021)
- [Accelerating Value Iteration with Anchoring](https://mlsanity.com/paper/2305.16569/) (2023)
- [Multi-Robot Active Mapping via Neural Bipartite Graph Matching](https://mlsanity.com/paper/2203.16319/) (2022)
- [Off-the-Grid MARL: Datasets with Baselines for Offline Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2302.00521/) (2023)
- [Modeling the Interaction between Agents in Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2102.06042/) (2021)
- [Putting Data at the Centre of Offline Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2409.12001/) (2024)
- [Equilibrium Inverse Reinforcement Learning for Ride-hailing Vehicle Network](https://mlsanity.com/paper/2102.06854/) (2021)
- [TOP-Former: A Multi-Agent Transformer Approach for the Team Orienteering Problem](https://mlsanity.com/paper/2311.18662/) (2023)
- [Learning Heuristics for Transit Network Design and Improvement with Deep Reinforcement Learning](https://mlsanity.com/paper/2404.05894/) (2024)
- [Pareto Frontier Approximation Network (PA-Net) to Solve Bi-objective TSP](https://mlsanity.com/paper/2203.01298/) (2022)
- [Value Iteration Networks with Gated Summarization Module](https://mlsanity.com/paper/2305.07039/) (2023)
- [Graph Learning-based Fleet Scheduling for Urban Air Mobility under Operational Constraints, Varying Demand & Uncertainties](https://mlsanity.com/paper/2401.04851/) (2024)
- [A Neural-Evolutionary Algorithm for Autonomous Transit Network Design](https://mlsanity.com/paper/2403.07917/) (2024)
- [Neural Bee Colony Optimization: A Case Study in Public Transit Network Design](https://mlsanity.com/paper/2306.00720/) (2023)
- [MANER: Multi-Agent Neural Rearrangement Planning of Objects in Cluttered Environments](https://mlsanity.com/paper/2306.06543/) (2023)

## Abstract (arXiv)

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.