[skip ci] fixing property decorator in sac

ml-agents/mlagents/trainers/ppo/trainer.py

         self.seed = seed
         self.policy: NNPolicy = None  # type: ignore
 
-    def should_still_train(self) -> bool:
-        """
-        Returns whether or not the trainer should train. A Trainer could
-        stop training if it wasn't training to begin with, or if max_steps
-        is reached.
-        """
-        return self.is_training and self.get_step <= self.trainer_settings.max_steps
-
     def _process_trajectory(self, trajectory: Trajectory) -> None:
         """
         Takes a trajectory and processes it, putting it into the update buffer.

ml-agents/mlagents/trainers/sac/trainer.py

             )
         )
 
+    @property
     def should_still_train(self) -> bool:
         """
         Returns whether or not the trainer should train. A Trainer could
         return self.is_training and self.steps_per_update * self.update_steps <= \
-               self.trainer_settings.max_steps
+               self.get_max_steps
 
     def _process_trajectory(self, trajectory: Trajectory) -> None:
         """

ml-agents/mlagents/trainers/trainer/rl_trainer.py

         """
         Saves training statistics to Tensorboard.
         """
-        self.stats_reporter.add_stat("Is Training", float(self.should_still_train()))
+        self.stats_reporter.add_stat("Is Training", float(self.should_still_train))
         self.stats_reporter.write_stats(int(step))
 
     @abc.abstractmethod