Yimin Tang, Xiao Xiong, Jingyi Xi, Jiaoyang Li, Erdem Bıyık, Sven Koenig

RAILGUN: A Unified Convolutional Policy for Multi-Agent Path Finding Across Different Environments and Tasks (Extended Abstract)

Introduction

Railgun: a unified convolutional policy for multi-agent path finding across different environments and tasks (extended abstract). RAILGUN: A unified, centralized convolutional policy for Multi-Agent Path Finding (MAPF). Solves collision-free pathfinding for multiple robots, generalizing across environments, tasks, and agents.

Abstract

Multi-Agent Path Finding (MAPF), which focuses on finding collision-free paths for multiple robots, is crucial for applications ranging from aerial swarms to warehouse automation. Solving MAPF is NP-hard so learning-based approaches for MAPF have gained attention, particularly those leveraging deep neural networks. Nonetheless, despite the community's continued efforts, all learning-based MAPF planners still rely on decentralized planning due to variability in the number of agents and map sizes. We have developed the first centralized learning-based policy for MAPF problem called RAILGUN. RAILGUN is not an agent-based policy but a map-based policy. By leveraging a CNN-based architecture, RAILGUN can generalize across different maps and handle any number of agents. We collect trajectories from rule-based methods to train our model in a supervised way. In experiments, RAILGUN outperforms most baseline methods and demonstrates great zero-shot generalization capabilities on various tasks, maps and agent numbers that were not seen in the training dataset.

Review

This extended abstract introduces RAILGUN, a novel learning-based policy designed to address the Multi-Agent Path Finding (MAPF) problem. The authors claim RAILGUN represents the first centralized learning-based policy, a significant departure from the decentralized approaches commonly adopted in the field due to challenges with varying agent numbers and map sizes. By proposing a map-based, CNN-driven architecture trained via supervised learning from rule-based methods, RAILGUN aims to overcome these limitations and offer a more unified solution. The abstract posits promising results regarding performance and zero-shot generalization, suggesting a potentially impactful contribution to MAPF research. The most compelling aspect of RAILGUN is its proposed centralized learning-based approach, which directly confronts a major bottleneck in existing learning-based MAPF solutions. The idea of a "map-based" policy, utilizing a CNN to process environmental information and handle a variable number of agents, presents an intriguing architectural choice that could significantly enhance scalability and generalization capabilities. The abstract highlights strong empirical performance, stating that RAILGUN outperforms most baseline methods and exhibits impressive zero-shot generalization across diverse environments, tasks, and agent counts not seen during training. These claims, if thoroughly substantiated, suggest RAILGUN could represent a substantial advancement in developing robust, adaptable, and practical learning-based MAPF planners. While the abstract is highly encouraging, the brevity inherent in an extended abstract naturally leaves several questions for the full paper. A deeper exposition on the specific CNN architecture and how it effectively encodes and processes the global map state to generate collision-free paths for multiple agents centrally would be beneficial. More detail is also needed regarding the supervised training regimen: specifically, the types of "rule-based methods" used for trajectory collection, their optimality guarantees, and how their characteristics might influence the learned policy's performance and generalization. The experimental section should include a detailed comparison against a wider range of state-of-the-art baselines, specifying the metrics used (e.g., success rate, path length, makespan, computation time) and the precise configurations (map types, agent densities, task complexities) for the reported zero-shot generalization. Clarification on the computational overhead of this centralized approach, especially for very large maps or high agent counts, would also strengthen the practical applicability claims.

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