def sample_forward_transitions(self, batch_size, batch_info,<EOL>forward_steps: int, discount_factor: float) -> Transitions:
probs, indexes, tree_idxs = self.backend.sample_batch_transitions(batch_size, forward_steps)<EOL>return self._get_transitions(<EOL>probs, indexes, tree_idxs, batch_info<EOL>)<EOL>
Sample transitions from replay buffer with _forward steps_.
That is, instead of getting a transition s_t -> s_t+1 with reward r,
get a transition s_t -> s_t+n with sum of intermediate rewards.

Used in a variant of Deep Q-Learning