[skip ci] change self.rank to global_values.get_rank()

ml-agents/mlagents/trainers/policy/tf_policy.py

     GaussianDistribution,
     MultiCategoricalDistribution,
 )
-from mlagents.tf_utils import global_values
 
 
 logger = get_logger(__name__)
         self.grads = None
         self.update_batch: Optional[tf.Operation] = None
         self.trainable_variables: List[tf.Variable] = []
-        self.rank = global_values.get_rank()
         if create_tf_graph:
             self.create_tf_graph()
 

ml-agents/mlagents/trainers/saver/tf_saver.py

         self.graph = None
         self.sess = None
         self.tf_saver = None
-        self.rank = global_values.get_rank()
 
     def register(self, module: Union[TFPolicy, TFOptimizer]) -> None:
         if isinstance(module, TFPolicy):
     def export(self, output_filepath: str, brain_name: str) -> None:
         # save model if there is only one worker or
         # only on worker-0 if there are multiple workers
-        if self.policy and self.rank is not None and self.rank != 0:
+        if (
+            self.policy
+            and global_values.get_rank() is not None
+            and global_values.get_rank() != 0
+        ):
             return
         export_policy_model(
             self.model_path, output_filepath, brain_name, self.graph, self.sess

ml-agents/mlagents/trainers/stats.py

         # If self-play, we want to print ELO as well as reward
         self.self_play = False
         self.self_play_team = -1
-        self.rank = global_values.get_rank()
 
     def write_stats(
         self, category: str, values: Dict[str, StatsSummary], step: int
         log_info.append(f"Time Elapsed: {elapsed_time:0.3f} s")
         if "Environment/Cumulative Reward" in values:
             stats_summary = values["Environment/Cumulative Reward"]
-            if self.rank is not None:
-                log_info.append(f"Rank: {self.rank}")
+            if global_values.get_rank() is not None:
+                log_info.append(f"Rank: {global_values.get_rank()}")
 
             log_info.append(f"Mean Reward: {stats_summary.mean:0.3f}")
             log_info.append(f"Std of Reward: {stats_summary.std:0.3f}")

ml-agents/mlagents/trainers/trainer_controller.py

         self.kill_trainers = False
         np.random.seed(training_seed)
         tf.set_random_seed(training_seed)
-        self.rank = global_values.get_rank()
 
     @timed
     def _save_models(self):
-        if self.rank is not None and self.rank != 0:
+        if global_values.get_rank() is not None and global_values.get_rank() != 0:
             return
 
         for brain_name in self.trainers.keys():
         """
         Saves models for all trainers.
         """
-        if self.rank is not None and self.rank != 0:
+        if global_values.get_rank() is not None and global_values.get_rank() != 0:
             return
 
         for brain_name in self.trainers.keys():