def construct_curr_info(self, next_info: BrainInfo) -> BrainInfo:
        """
        Constructs a BrainInfo which contains the most recent previous experiences for all agents info
        which correspond to the agents in a provided next_info.
        :BrainInfo next_info: A t+1 BrainInfo.
        :return: curr_info: Reconstructed BrainInfo to match agents of next_info.
        """
        visual_observations = [[]]
        vector_observations = []
        text_observations = []
        memories = []
        rewards = []
        local_dones = []
        max_reacheds = []
        agents = []
        prev_vector_actions = []
        prev_text_actions = []
        action_masks = []
        for agent_id in next_info.agents:
            agent_brain_info = self.training_buffer[agent_id].last_brain_info
            if agent_brain_info is None:
                agent_brain_info = next_info
            agent_index = agent_brain_info.agents.index(agent_id)
            for i in range(len(next_info.visual_observations)):
                visual_observations[i].append(agent_brain_info.visual_observations[i][agent_index])
            vector_observations.append(agent_brain_info.vector_observations[agent_index])
            text_observations.append(agent_brain_info.text_observations[agent_index])
            if self.policy.use_recurrent:
                if len(agent_brain_info.memories) > 0:
                    memories.append(agent_brain_info.memories[agent_index])
                else:
                    memories.append(self.policy.make_empty_memory(1))
            rewards.append(agent_brain_info.rewards[agent_index])
            local_dones.append(agent_brain_info.local_done[agent_index])
            max_reacheds.append(agent_brain_info.max_reached[agent_index])
            agents.append(agent_brain_info.agents[agent_index])
            prev_vector_actions.append(agent_brain_info.previous_vector_actions[agent_index])
            prev_text_actions.append(agent_brain_info.previous_text_actions[agent_index])
            action_masks.append(agent_brain_info.action_masks[agent_index])
        if self.policy.use_recurrent:
            memories = np.vstack(memories)
        curr_info = BrainInfo(visual_observations, vector_observations, text_observations,
                              memories, rewards, agents, local_dones, prev_vector_actions,
                              prev_text_actions, max_reacheds, action_masks)
        return curr_info