Multi-Agent Routing Value Iteration Network
This addresses the problem of efficient and adaptive routing for applications like fleet management and robot swarms, though it is incremental as it builds on existing graph neural network and value iteration methods.
The paper tackles multi-agent routing in realistic environments with sparse graphs and dynamic traffic, proposing a graph neural network model that significantly outperforms traditional solvers in total cost and runtime, and generalizes to more agents and nodes.
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.