分位数传播规划：面向预算受限的尾部风险评估
Quantile Propagation Planning for Budgeted Tail-Risk Evaluation
Weijia Han, Zhi Huang, Lisha Qu, Yuanyuan Tan, Jiayue Meng, Wenjun Chen, Liying Liang, Bangjie Deng
AI 速览：提出分位数传播规划（QPP），用少量固定扰动分位数经动力学传播并积分代价曲线，在有限模型评估预算下同时评估均值与尾部风险。
Risk-aware planning must identify rare, costly outcomes using only a limited number of model evaluations. Monte Carlo rollouts spend this budget on noisy tail estimates, while planning from the mean can miss the tail entirely. We introduce quantile propagation planning (QPP), which propagates a small, fixed set of disturbance quantiles through the dynamics and integrates the resulting cost curve to evaluate both mean and tail risk. The method is exact at the propagated levels, converges as the quantile grid is refined, and admits a one-sided conservatism guarantee under stated structural conditions. In controlled overload and inventory tasks, QPP reduces realized tail cost and constraint violations relative to mean-rollout planning. On overload, it remains competitive with sampled evaluators that use substantially more trajectories. These results position QPP as a transparent, budget-controlled approach to tail-risk evaluation.