Preprint / Version 1

CG-XMARL v4.1 Technical Specification

##article.authors##

  • Behnam Heydari Isfahan University of Technology (IUT)

DOI:

https://doi.org/10.31224/6429

Keywords:

multi-agent reinforcement learning, Autonomous flight control, Formation control, Simulation, Transformers and Attention Mechanisms, multi-layered model

Abstract

CG-XMARL v4.1 is a military-grade multi-agent reinforcement learning system for autonomous UAV formation control integrated with DCS World 2.9. The system implements a 3-UAV wedge formation pursuit control architecture with advanced consensus-based virtual structure (CBVS) guidance, hierarchical finite state machines, and a planned HGAT-MAPPO-WM (Hierarchical Graph Attention Transformer with Multi-Agent Proximal Policy Optimization and World Model) neural architecture.

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Posted

2026-02-05

License

Copyright (c) 2026 Behnam Heydari

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.