标题： CatlNet：从CaTL+规范中学习通信和协调策略 显示英文标题

标题： CatlNet: Learning Communication and Coordination Policies from CaTL+ Specifications

摘要： 本文中，我们提出了一种基于学习的框架，以同时学习在复杂任务需求下异构多智能体系统（MAS）的通信和分布式控制策略，这些任务需求来自能力时态逻辑加（CaTL+）规范。 两个策略均使用一种称为CatlNet的新神经网络模型进行训练、实施和部署。 利用CaTL+的鲁棒性度量，我们在中心位置训练CatlNet以最大化该度量，其中所有智能体共享网络参数，使CatlNet能够轻松扩展到大型团队。 然后可以分布式部署CatlNet。 还引入了一个计划修复算法来指导CatlNet的训练，提高训练效率和CatlNet的整体性能。 CatlNet方法在仿真中进行了测试，结果表明，经过训练后，CatlNet可以在线引导去中心化的MAS系统以高成功率满足CaTL+规范。 显示英文摘要

摘要： In this paper, we propose a learning-based framework to simultaneously learn the communication and distributed control policies for a heterogeneous multi-agent system (MAS) under complex mission requirements from Capability Temporal Logic plus (CaTL+) specifications. Both policies are trained, implemented, and deployed using a novel neural network model called CatlNet. Taking advantage of the robustness measure of CaTL+, we train CatlNet centrally to maximize it where network parameters are shared among all agents, allowing CatlNet to scale to large teams easily. CatlNet can then be deployed distributedly. A plan repair algorithm is also introduced to guide CatlNet's training and improve both training efficiency and the overall performance of CatlNet. The CatlNet approach is tested in simulation and results show that, after training, CatlNet can steer the decentralized MAS system online to satisfy a CaTL+ specification with a high success rate.