Adds experiences to each agent's experience history.
        :param curr_info: Current AllBrainInfo (Dictionary of all current brains and corresponding BrainInfo).
        :param next_info: Next AllBrainInfo (Dictionary of all current brains and corresponding BrainInfo).
        :param take_action_outputs: The outputs of the take action method.

def add_experiences(self, curr_info: AllBrainInfo, next_info: AllBrainInfo,
                        take_action_outputs):
        """
        Adds experiences to each agent's experience history.
        :param curr_info: Current AllBrainInfo (Dictionary of all current brains and corresponding BrainInfo).
        :param next_info: Next AllBrainInfo (Dictionary of all current brains and corresponding BrainInfo).
        :param take_action_outputs: The outputs of the take action method.
        """

        # Used to collect teacher experience into training buffer
        info_teacher = curr_info[self.brain_to_imitate]
        next_info_teacher = next_info[self.brain_to_imitate]
        for agent_id in info_teacher.agents:
            self.demonstration_buffer[agent_id].last_brain_info = info_teacher

        for agent_id in next_info_teacher.agents:
            stored_info_teacher = self.demonstration_buffer[agent_id].last_brain_info
            if stored_info_teacher is None:
                continue
            else:
                idx = stored_info_teacher.agents.index(agent_id)
                next_idx = next_info_teacher.agents.index(agent_id)
                if stored_info_teacher.text_observations[idx] != "":
                    info_teacher_record, info_teacher_reset = \
                        stored_info_teacher.text_observations[idx].lower().split(",")
                    next_info_teacher_record, next_info_teacher_reset = \
                    next_info_teacher.text_observations[idx]. \
                        lower().split(",")
                    if next_info_teacher_reset == "true":
                        self.demonstration_buffer.reset_update_buffer()
                else:
                    info_teacher_record, next_info_teacher_record = "true", "true"
                if info_teacher_record == "true" and next_info_teacher_record == "true":
                    if not stored_info_teacher.local_done[idx]:
                        for i in range(self.policy.vis_obs_size):
                            self.demonstration_buffer[agent_id]['visual_obs%d' % i] \
                                .append(stored_info_teacher.visual_observations[i][idx])
                        if self.policy.use_vec_obs:
                            self.demonstration_buffer[agent_id]['vector_obs'] \
                                .append(stored_info_teacher.vector_observations[idx])
                        if self.policy.use_recurrent:
                            if stored_info_teacher.memories.shape[1] == 0:
                                stored_info_teacher.memories = np.zeros(
                                    (len(stored_info_teacher.agents),
                                     self.policy.m_size))
                            self.demonstration_buffer[agent_id]['memory'].append(
                                stored_info_teacher.memories[idx])
                        self.demonstration_buffer[agent_id]['actions'].append(
                            next_info_teacher.previous_vector_actions[next_idx])

        super(OnlineBCTrainer, self).add_experiences(curr_info, next_info, take_action_outputs)