arXiv cs.AI· \.Ibrahim O\u{g}uz \c{C}etinkaya, Sajad Khodadadian, Taylan Topcu·· 13 小时前AI 评分22
多场景强化学习如何提升体系之体系的任务协同鲁棒性
Brittle by Design? Multi-Scenario Reinforcement Learning for Robust Mission Coordination in Systems of Systems
AI 导读
研究提出将高保真数字任务模型与强化学习结合的任务协同方法，用 PPO 训练协调器实现自适应任务分配，并在三场景空中消防任务中验证。所有训练后的协调器均显著优于随机与启发式基线，但仅在宽松场景训练的协调器迁移到更严苛场景时会退化至随机水平。相同训练预算下，同时在两个场景训练的协调器在各场景中匹配专用协调器，并显著优于其他策略。
正文 · 原文
Abstract:As systems engineering (SE) shifts from monolithic systems to Systems of Systems, Mission Engineering (ME) has emerged as a new line of thinking in SE. Mission environments are uncertain and dynamic, and outcomes depend on how mission assets interact with them, rendering static architectures brittle and calling for flexible yet analytically rigorous ME approaches. We propose an intelligent mission coordination methodology integrating high-fidelity digital mission models with Reinforcement Learning (RL) for adaptive task allocation. Using a Digital Engineering (DE) infrastructure comprising a digital mission model and agent-based simulation, we formulate mission tactics management as a Markov Decision Process and train an RL coordinator with Proximal Policy Optimization to map mission states to tactical actions from realized outcomes. An aerial firefighting mission with three scenarios demonstrates the approach. First, all trained coordinators significantly outperform random and heuristic baselines. Second, single-scenario coordinators can be brittle: the coordinator trained on the more forgiving scenario degrades to random tactics performance in more demanding scenarios, whereas the demanding-scenario coordinator retains most of its advantage when transferred. Third, within the same training budget, a coordinator trained on both scenarios concurrently matches the scenario-specific coordinators in their own scenarios and significantly outperforms every other policy. Sensitivity analyses on ignition location, weather, scenario, and training curricula show that the variability represented during training shapes post-deployment performance. This proof of concept shows DE-enabled mission simulations combined with RL offer a reusable methodology for improving ME practice, which can be extended to fleet design and selection from a mission-first perspective.
| Subjects: | Systems and Control (eess.SY); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.28818 [eess.SY] |
| (or arXiv:2610.28818v1 [eess.SY] for this version) | |
| https://doi.org/10.48550/arXiv.2610.28818 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ibrahim Cetinkaya [view email]
[v1]
Tue, 20 Oct 2026 18:40:12 UTC (7,874 KB)
来源：arXiv cs.AI · arxiv.org