通过流模型隐空间优化采样，提升罕见事件估计效率。
Enhanced Importance Sampling through Latent Space Exploration in Normalizing Flows
- 在流模型隐空间中动态更新提议分布
- 在机器人自主竞速与飞机避障任务中显著提升采样效率
- 适合需要精准模拟罕见场景的强化学习与安全系统研究
重要性采样是一种用于蒙特卡洛模拟的罕见事件仿真技术，通过调整采样分布以聚焦目标罕见事件。通过对采样点赋予适当权重，可更高效地估计罕见事件或分布尾部。然而，当提议分布无法有效覆盖目标分布时，重要性采样会失效。本文提出一种新方法：在归一化流（normalizing flow）的隐空间中更新提议分布，以实现更高效的采样。归一化流学习从目标分布到简单潜空间的可逆映射，潜空间更易于探索以寻找合适的提议分布，再通过可逆映射将样本恢复至目标空间。我们在模拟的机器人应用中验证该方法，包括自主竞速和飞机地面避碰任务。
原文摘要 · Abstract (English)
Importance sampling is a rare event simulation technique used in Monte Carlo simulations to bias the sampling distribution towards the rare event of interest. By assigning appropriate weights to sampled points, importance sampling allows for more efficient estimation of rare events or tails of distributions. However, importance sampling can fail when the proposal distribution does not effectively cover the target distribution. In this work, we propose a method for more efficient sampling by updating the proposal distribution in the latent space of a normalizing flow. Normalizing flows learn an invertible mapping from a target distribution to a simpler latent distribution. The latent space can be more easily explored during the search for a proposal distribution, and samples from the proposal distribution are recovered in the space of the target distribution via the invertible mapping. We empirically validate our methodology on simulated robotics applications such as autonomous racing and aircraft ground collision avoidance.
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