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CatlNet: Learning Communication and Coordination Policies from CaTL+ Specifications

arXiv 2212.11792 ↗First posted 30 Nov 2022cs.LG cs.SY eess.SYcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 37 references that are in the map. First posted on arXiv 30 Nov 2022.

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20 of 37 references.

References · 20

  1. 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 561
  2. 2016Learning Multiagent Communication with Backpropagationcited by 355
  3. 2018Learning Attentional Communication for Multi-Agent Cooperationcited by 170
  4. 2017Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Gamescited by 93
  5. 2016Q-Learning for Robust Satisfaction of Signal Temporal Logic Specificationscited by 65
  6. 2020Backpropagation through Signal Temporal Logic Specifications: Infusing Logical Structure into Gradient-Based Methodscited by 51
  7. 2021Modular Deep Reinforcement Learning for Continuous Motion Planning with Temporal Logiccited by 38
  8. 2021Multi-Agent Reinforcement Learning with Temporal Logic Specificationscited by 27
  9. 2018STyLuS*: A Temporal Logic Optimal Control Synthesis Algorithm for Large-Scale Multi-Robot Systemscited by 24
  10. 2015Robust Temporal Logic Model Predictive Controlcited by 21
  11. 2020A Smooth Robustness Measure of Signal Temporal Logic for Symbolic Controlcited by 21
  12. 2022Multi-agent Motion Planning from Signal Temporal Logic Specificationscited by 21
8 more
  1. 2020Recurrent Neural Network Controllers for Signal Temporal Logic Specifications Subject to Safety Constraintscited by 18
  2. 2019An Abstraction-Free Method for Multi-Robot Temporal Logic Optimal Control Synthesiscited by 11
  3. 2022Semi-Supervised Trajectory-Feedback Controller Synthesis for Signal Temporal Logic Specificationscited by 11
  4. 2017Model Predictive Control for Signal Temporal Logic Specificationcited by 8
  5. 2018Multirobot Coordination with Counting Temporal Logicscited by 7
  6. 2022Robust Multi-Agent Coordination from CaTL+ Specificationscited by 7
  7. 2016Census Signal Temporal Logic Inference for Multi-Agent Group Behavior Analysiscited by 6
  8. 2022Distributed Control using Reinforcement Learning with Temporal-Logic-Based Reward Shapingcited by 2

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