论文标题
CATLNET：从CATL+规格中学习沟通和协调政策
CatlNet: Learning Communication and Coordination Policies from CaTL+ Specifications
论文作者
论文摘要
在本文中，我们提出了一个基于学习的框架，以同时学习在功能时间逻辑Plus（CATL+）规格的复杂任务要求下，在复杂的任务要求下，为异构多机构系统（MAS）学习沟通和分布式控制策略。这两种政策均经过培训，实施和部署，使用新型的神经网络模型，称为CATLNET。利用CATL+的鲁棒性度量，我们集中训练CATLNET，以最大程度地将网络参数共享在所有代理之间，从而使Catlnet可以轻松扩展到大型团队。然后可以分发catlnet。还引入了计划维修算法，以指导CATLNET的培训，并提高训练效率和CATLNET的整体性能。在模拟中测试了CATLNET方法，结果表明，经过训练，CATLNET可以在线引导分散的MAS系统以满足CATL+规格，并具有很高的成功率。
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.
