  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.
