通过在 Normalizing Flows 中进行潜空间探索增强重要性采样
Enhanced Importance Sampling through Latent Space Exploration in Normalizing Flows
AI总结：
针对重要性采样中提议分布覆盖不足的问题，本文提出在 normalizing flow 潜空间中更新并探索提议分布，再经可逆映射恢复目标空间样本，并在机器人仿真任务中验证其有效性。
摘要：
重要性采样是蒙特卡洛模拟中使用的一种稀有事件模拟技术，用于使采样分布偏向所关注的稀有事件。通过为采样点分配适当的权重，重要性采样能够更高效地估计稀有事件或分布尾部。然而，当提议分布无法有效覆盖目标分布时，重要性采样可能失效。在本工作中，我们提出了一种通过在 normalizing flow 的潜空间中更新提议分布来实现更高效采样的方法。Normalizing flows 学习从目标分布到更简单潜分布的可逆映射。在搜索提议分布期间，潜空间可以更容易地被探索，而来自提议分布的样本则通过该可逆映射在目标分布空间中恢复。我们在自动驾驶赛车和飞机地面防撞等仿真机器人应用上对所提方法进行了实证验证。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
