CatlNet: Learning Communication and Coordination Policies from CaTL+ Specifications
This addresses coordination challenges in multi-agent systems for applications like robotics or autonomous systems, but appears incremental as it builds on existing CaTL+ specifications and learning methods.
The paper tackles the problem of learning communication and coordination policies for heterogeneous multi-agent systems under complex mission requirements specified by CaTL+, using a neural network model called CatlNet, and results show it achieves a high success rate in satisfying specifications after training.
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.