Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation
学习部分可观测下的鲁棒渗透测试策略：系统评估
机构 * Cyber Defence Lab, CISS Department Royal Military Academy（国防网络安全实验室，信息与系统科学系皇家军事学院） ； AI Lab, Department of Computer Science Vrije Universiteit Brussel（人工智能实验室，计算机科学系自由大学布鲁塞尔）
AI总结 针对部分可观测的渗透测试问题，系统评估了多种PPO变体（如帧堆叠、历史观测增强、LSTM/TrXL架构）在主机网络中的性能，发现历史聚合策略收敛速度提升四倍，并揭示了策略的定性差异。
Comments Published in Transactions on Machine Learning Research (TMLR) https://openreview.net/forum?id=YkUV7wfk19. 25 pages, 8 figures. Code and StochNASim environment are available at https://github.com/raphsimon/StochNASim
Journal ref Transactions on Machine Learning Research, 2026
