tensorforce/core/memories/deprecated/deprecated_prioritized_replay.py
def sample_minibatch(self, batch_size):
        """
        Sample minibatch of size batch_size.
        """
        pool_size = len(self)
        if pool_size == 0:
            return []

        delta_p = self._memory[0] / batch_size
        chosen_idx = []
        # if all priorities sum to ~0  choose randomly otherwise random sample
        if abs(self._memory[0]) < util.epsilon:
            chosen_idx = np.random.randint(self._capacity - 1, self._capacity - 1 + len(self), size=batch_size).tolist()
        else:
            for i in xrange(batch_size):
                lower = max(i * delta_p, 0)
                upper = min((i + 1) * delta_p, self._memory[0])
                p = random.uniform(lower, upper)
                chosen_idx.append(self._sample_with_priority(p))
        return [(i, self._memory[i]) for i in chosen_idx]
def sample_minibatch(self, batch_size):
        """
        Sample minibatch of size batch_size.
        """
        pool_size = len(self)
        if pool_size == 0:
            return []

        delta_p = self._memory[0] / batch_size
        chosen_idx = []
        # if all priorities sum to ~0  choose randomly otherwise random sample
        if abs(self._memory[0]) < util.epsilon:
            chosen_idx = np.random.randint(self._capacity - 1, self._capacity - 1 + len(self), size=batch_size).tolist()
        else:
            for i in xrange(batch_size):
                lower = max(i * delta_p, 0)
                upper = min((i + 1) * delta_p, self._memory[0])
                p = random.uniform(lower, upper)
                chosen_idx.append(self._sample_with_priority(p))
        return [(i, self._memory[i]) for i in chosen_idx]
Sample minibatch of size batch_size.