Existing methods for safe multi-agent control using logic specifications like Signal Temporal Logic (STL) often face scalability issues. This is because they rely either on single-agent perspectives or on Mixed Integer Linear Programming (MILP)-based planners, which are complex to optimize. These methods have proven to be computationally expensive and inefficient when dealing with a large number of agents. To address these limitations, we present a new scalable approach to multi-agent control in this setting. Our method treats the relationships between agents using a graph structure rather than in terms of a single-agent perspective. Moreover, it combines a multi-agent collision avoidance controller with a Graph Neural Network (GNN) based planner, models the system in a decentralized fashion, and trains on STL-based objectives to generate safe and efficient plans for multiple agents, thereby optimizing the satisfaction of complex temporal specifications while also facilitating multi-agent collision avoidance. Our experiments show that our approach significantly outperforms existing methods that use a state-of-the-art MILP-based planner in terms of scalability and performance. The project website is https://jeappen.com/mastl-gcbf-website/ and the code is at https://github.com/jeappen/mastl-gcbf .

翻译：现有基于信号时序逻辑（STL）等逻辑规范的安全多智能体控制方法常面临可扩展性问题。这主要源于其要么依赖单智能体视角，要么采用基于混合整数线性规划（MILP）的规划器，而这些方法在优化上较为复杂。事实证明，在处理大量智能体时，这些方法计算成本高昂且效率低下。为应对这些局限，本文提出一种在此场景下可扩展的多智能体控制新方法。我们的方法采用图结构而非单智能体视角来建模智能体间关系。此外，该方法将多智能体碰撞避免控制器与基于图神经网络（GNN）的规划器相结合，以去中心化方式对系统进行建模，并基于STL目标进行训练，从而为多智能体生成安全高效的规划，在优化复杂时序规范满足度的同时，也促进了多智能体碰撞避免。实验表明，在可扩展性与性能方面，我们的方法显著优于采用最先进的基于MILP规划器的现有方法。项目网站为 https://jeappen.com/mastl-gcbf-website/ ，代码位于 https://github.com/jeappen/mastl-gcbf 。