Automatic Algorithm Selection In Multi-agent Pathfinding
This addresses the challenge of algorithm selection in MAPF for researchers and practitioners, but it is incremental as it applies an existing deep learning method to a known problem.
The paper tackles the problem of selecting the best algorithm for multi-agent pathfinding (MAPF) instances by using deep learning to automatically choose from a portfolio, resulting in an approach that outperforms any individual algorithm in the portfolio.
In a multi-agent pathfinding (MAPF) problem, agents need to navigate from their start to their goal locations without colliding into each other. There are various MAPF algorithms, including Windowed Hierarchical Cooperative A*, Flow Annotated Replanning, and Bounded Multi-Agent A*. It is often the case that there is no a single algorithm that dominates all MAPF instances. Therefore, in this paper, we investigate the use of deep learning to automatically select the best MAPF algorithm from a portfolio of algorithms for a given MAPF problem instance. Empirical results show that our automatic algorithm selection approach, which uses an off-the-shelf convolutional neural network, is able to outperform any individual MAPF algorithm in our portfolio.