Robust Multi-Agent Reinforcement Learning for Small UAS Separation Assurance under GPS Degradation and Spoofing
📰 ArXiv cs.AI
Robust Multi-Agent Reinforcement Learning ensures separation assurance for small UAS under GPS degradation and spoofing
Action Steps
- Formulate the problem as a zero-sum game between agents and an adversary
- Implement Multi-Agent Reinforcement Learning (MARL) to learn robust policies
- Evaluate the performance of MARL under various GPS degradation and spoofing scenarios
- Refine the MARL algorithm to improve separation assurance and adapt to changing conditions
Who Needs to Know This
This research benefits teams of ai-engineers, ml-researchers, and software-engineers working on autonomous systems, particularly those involved in drone development and air traffic control, as it provides a robust method for maintaining separation assurance in challenging environments
Key Insight
💡 MARL can effectively mitigate the effects of GPS corruption on air traffic state estimation
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🚁💻 Robust MARL for sUAS separation assurance under GPS degradation & spoofing
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