[skip ci] save model on worker zero only

ml-agents/mlagents/tf_utils/globals.py

 
 def get_rank() -> Optional[int]:
     return _rank
+
+
+def broadcast_variables() -> bool:
+    return True if _rank is not None else False

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

     GaussianDistribution,
     MultiCategoricalDistribution,
 )
+from mlagents.tf_utils.globals import get_rank, broadcast_variables
 
 
 logger = get_logger(__name__)
         self.grads = None
         self.update_batch: Optional[tf.Operation] = None
         self.trainable_variables: List[tf.Variable] = []
+        self.rank = get_rank()
         if create_tf_graph:
             self.create_tf_graph()
 
             self._load_graph(self.model_path, reset_global_steps=reset_steps)
         else:
             self._initialize_graph()
+        # broadcast initial weights from worker-0
+        if broadcast_variables():
+            self.sess.run(hvd.broadcast_global_variables(0))
 
     def get_weights(self):
         with self.graph.as_default():
         :param output_filepath: path (without suffix) for the model file(s)
         :param settings: SerializationSettings for how to save the model.
         """
+        # save model if there is only one worker or
+        # only on worker-0 if there are multiple workers
+        if self.rank is not None and self.rank != 0:
+            return
         export_policy_model(output_filepath, settings, self.graph, self.sess)
 
     def update_normalization(self, vector_obs: np.ndarray) -> None: