code: def process_experiences(self, current_info: AllBrainInfo, next_info: AllBrainInfo):
          
          info_student = next_info[self.brain_name]
          for l in range(len(info_student.agents)):
              if info_student.local_done[l]:
                  agent_id = info_student.agents[l]
                  self.stats['Environment/Cumulative Reward'].append(
                      self.cumulative_rewards.get(agent_id, 0))
                  self.stats['Environment/Episode Length'].append(
                      self.episode_steps.get(agent_id, 0))
                  self.cumulative_rewards[agent_id] = 0
                  self.episode_steps[agent_id] = 0
docs: Checks agent histories for processing condition, and processes them as necessary.
          Processing involves calculating value and advantage targets for model updating step.
          :param current_info: Current AllBrainInfo
          :param next_info: Next AllBrainInfo