在看莫烦python的RL源码时，他的DDPG记忆库Memory的实现是这样写的：

class Memory(object):
    def __init__(self, capacity, dims):
        self.capacity = capacity
        self.data = np.zeros((capacity, dims))
        self.pointer = 0

    def store_transition(self, s, a, r, s_):
        transition = np.hstack((s, a, [r], s_))
        index = self.pointer % self.capacity  # replace the old memory with new memory
        self.data[index, :] = transition
        self.pointer += 1

    def sample(self, n):
        assert self.pointer >= self.capacity, 'Memory has not been fulfilled'
        indices = np.random.choice(self.capacity, size=n)
        return self.data[indices, :]

其中sample方法用assert断言pointer >= capacity，也就是说Memory必须满了才能学习。

我在设计一种方案，一开始往记忆库里存比较好的transition(也就是reward比较高的)，要是等记忆库填满再学习好像有点浪费，因为会在填满之后很快被差的transition所替代，甚至好的transition不能填满Memory，从而不能有效学习好的经验。

此时就需要关注np.random.choice方法了，看源码解释：

def choice(a, size=None, replace=True, p=None): # real signature unknown; restored from __doc__
    """
    choice(a, size=None, replace=True, p=None)
    
            Generates a random sample from a given 1-D array
    
                    .. versionadded:: 1.7.0
    
            Parameters
            -----------
            a : 1-D array-like or int
                If an ndarray, a random sample is generated from its elements.
                If an int, the random sample is generated as if a were np.arange(a)
            size : int or tuple of ints, optional
                Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
                ``m * n * k`` samples are drawn.  Default is None, in which case a
                single value is returned.
            replace : boolean, optional
                Whether the sample is with or without replacement
            p : 1-D array-like, optional
                The probabilities associated with each entry in a.
                If not given the sample assumes a uniform distribution over all
                entries in a.
    
            Returns
            --------
            samples : single item or ndarray
                The generated random samples

主要第一个参数为ndarray，如果给的是int，np会自动将其通过np.arange(a)转换为ndarray。

此处主要关注的是，a(我们使用int)< size时，np会怎么取？

上代码测试

import numpy as np

samples = np.random.choice(3, 5)
print(samples)

输出：

[2 1 2 1 1]

所以，是会从np.array(a)重复取，可以推断出，np.random.choice是“有放回地取”（具体我也没看源码，从重复情况来看，至少a<size时是这样的）

然后我分别测试了np.random.choice(5, 5)、np.random.choice(10, 5)等。多试几次会发现samples中确实是会有重复的。：

import numpy as np

samples = np.random.choice(10, 5)
print(samples)

[3 4 3 4 5]