idxes: np.array Array of shape (batch_size,) and dtype np.int32 idexes in buffer of sampled experiences

idxes是一个numpy数组，形状为(batch_size,)，数据类型为np.int32，用于表示从经验池中抽取的样本经验在经验池中的索引。在经验回放中，我们通常从经验池中随机抽取一些经验转换作为训练数据。idxes记录了这些抽样的经验在经验池中的索引，以便在更新模型参数时能够正确地从经验池中获取对应的经验转换。

相关问题

weights: np.array Array of shape (batch_size,) and dtype np.float32 denoting importance weight of each sampled transition

weights是一个numpy数组，形状为(batch_size,)，数据类型为np.float32，用于表示每个采样转换的重要性权重。在经验回放中，我们通常使用重要性采样来抽取经验，而这些采样的转换并不是均匀分布的。因此，我们需要为每个采样转换分配一个重要性权重，以便更好地利用这些转换。重要性权重越高，表示该转换对训练的贡献越大。

class TimeWindowSequence(Sequence): def __init__(self, x, y, batch_size, window_size): self.x = x self.y = y self.batch_size = batch_size self.window_size = window_size self.window_count = int(np.ceil(x.shape[0] / window_size)) def __len__(self): return int(np.ceil(self.x.shape[0] / self.batch_size)) def __getitem__(self, idx): batch_x = np.zeros((self.batch_size, self.window_size, self.x.shape[1])) batch_y = np.zeros((self.batch_size, self.y.shape[1])) for i in range(self.batch_size): j = idx * self.batch_size + i if j >= self.window_count: break window_x = self.x[j*self.window_size:(j+1)*self.window_size, :] window_y = self.y[j*self.window_size:(j+1)*self.window_size, :] batch_x[i, :window_x.shape[0], :] = window_x batch_y[i, :] = window_y[-1, :] return batch_x, batch_y出现

这段代码中似乎缺少了一个缩进，导致 `return` 语句在 `for` 循环内部。可以尝试将 `return` 语句缩进到 `for` 循环外部，如下所示：

class TimeWindowSequence(Sequence):
    def __init__(self, x, y, batch_size, window_size):
        self.x = x
        self.y = y
        self.batch_size = batch_size
        self.window_size = window_size
        self.window_count = int(np.ceil(x.shape[0] / window_size))

    def __len__(self):
        return int(np.ceil(self.x.shape[0] / self.batch_size))

    def __getitem__(self, idx):
        batch_x = np.zeros((self.batch_size, self.window_size, self.x.shape[1]))
        batch_y = np.zeros((self.batch_size, self.y.shape[1]))
        for i in range(self.batch_size):
            j = idx * self.batch_size + i
            if j >= self.window_count:
                break
            window_x = self.x[j*self.window_size:(j+1)*self.window_size, :]
            window_y = self.y[j*self.window_size:(j+1)*self.window_size, :]
            batch_x[i, :window_x.shape[0], :] = window_x
            batch_y[i, :] = window_y[-1, :]
        return batch_x, batch_y

这样应该就可以解决这个问题了。