def _sample_batch_prioritized(self, segment_tree, batch_size, history, forward_steps=<NUM_LIT:1>):
p_total = segment_tree.total()<EOL>segment = p_total / batch_size<EOL>batch = [<EOL>self._get_sample_from_segment(segment_tree, segment, i, history, forward_steps)<EOL>for i in range(batch_size)<EOL>]<EOL>probs, idxs, tree_idxs = zip(*batch)<EOL>return np.array(probs), np.array(idxs), np.array(tree_idxs)<EOL>
Return indexes of the next sample in from prioritized distribution