The survey examines the current state of research on applying machine learning (ML) techniques to model air combat behavior. Key insights:
Behavior modeling is crucial for simulation-based pilot training, mission planning, and tactics development, but traditional manual methods are labor-intensive and prone to losing domain knowledge.
Advancements in reinforcement learning (RL) and imitation learning (IL) have demonstrated the potential for agents to learn complex air combat behavior from data, which could be faster and more scalable than manual methods.
The most common ML-based behavior models are neural networks, which can capture general patterns from complex data and allow iterative improvements. Actor-critic methods like DDPG, PPO, and SAC are widely used to train these neural network models.
Air-to-air combat, especially dogfighting, is the predominant focus, but beyond-visual-range (BVR) scenarios are gaining importance as missile and sensor ranges increase.
Multi-agent learning, hierarchical behavior models, and initiatives for standardization and research collaboration are identified as key areas to address current challenges and guide future development of comprehensive, adaptable, and realistic ML-based air combat behavior models.
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by Patrick Ribu... alle arxiv.org 04-23-2024
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