agent/papers/items/2026-2603-28900-robust-multi-agent-reinforcement-learning-for-small-uas-separation-assurance-und.md

Paper: Robust Multi-Agent Reinforcement Learning for Small UAS Separation Assurance under GPS Degradation and Spoofing

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type: paper title: Robust Multi-Agent Reinforcement Learning for Small UAS Separation Assurance under GPS Degradation and Spoofing authors: Alex Zongo, Filippos Fotiadis, Ufuk Topcu, Peng Wei year: 2026 venue: arXiv url: https://arxiv.org/abs/2603.28900 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-03-30 updated_at: 2026-03-30 status: queued relevance: high topics:

• agent-evaluation
• agent-safety
• multi-agent
• world-model methods:

benchmarks:

models:

datasets:

• cs.RO
• cs.AI
• cs.LG
• eess.SY related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 13 collection_queries: agent-safety

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: agent-safety
• inferred topics: agent-evaluation, agent-safety, multi-agent, world-model
• arXiv categories: cs.RO, cs.AI, cs.LG, eess.SY
• collection score: 13

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2603.28900