code: def execute(self, action):
          
          adjusted_action = list()
          for action_spec in self.level.action_spec():
              if action_spec['min'] == -1 and action_spec['max'] == 1:
                  adjusted_action.append(action[action_spec['name']] - 1)
              else:
                  adjusted_action.append(action[action_spec['name']])  # clip?
          action = np.array(adjusted_action, dtype=np.intc)
  
          reward = self.level.step(action=action, num_steps=self.repeat_action)
          state = self.level.observations()['RGB_INTERLACED']
          terminal = not self.level.is_running()
          return state, terminal, reward
docs: Pass action to universe environment, return reward, next step, terminal state and
          additional info.
  
          :param action: action to execute as numpy array, should have dtype np.intc and should adhere to
              the specification given in DeepMindLabEnvironment.action_spec(level_id)
          :return: dict containing the next state, the reward, and a boolean indicating if the
              next state is a terminal state