sample（）和rsample（）之间有什么区别？

Question

当我从PyTorch中的分布中取样时，sample和rsample似乎给出了类似的结果：

import torch, seaborn as sns

x = torch.distributions.Normal(torch.tensor([0.0]), torch.tensor([1.0]))

^{tb1}$

我应该什么时候使用sample()，什么时候应该使用rsample()

Answer 1

使用rsample允许pathwise derivatives：

The other way to implement these stochastic/policy gradients would be to use the reparameterization trick from the rsample() method, where the parameterized random variable can be constructed via a parameterized deterministic function of a parameter-free random variable. The reparameterized sample therefore becomes differentiable.