diff --git a/ml-agents/mlagents/trainers/ghost/trainer.py b/ml-agents/mlagents/trainers/ghost/trainer.py
index f49a643574..52e5e45643 100644
--- a/ml-agents/mlagents/trainers/ghost/trainer.py
+++ b/ml-agents/mlagents/trainers/ghost/trainer.py
@@ -195,26 +195,7 @@ def _process_trajectory(self, trajectory: Trajectory) -> None:
         i.e. in asymmetric games. We assume the last reward determines the winner.
         :param trajectory: Trajectory.
         """
-        if (
-            trajectory.done_reached
-            and trajectory.all_group_dones_reached
-            and not trajectory.interrupted
-        ):
-            # Assumption is that final reward is >0/0/<0 for win/draw/loss
-            final_reward = (
-                trajectory.steps[-1].reward + trajectory.steps[-1].group_reward
-            )
-            result = 0.5
-            if final_reward > 0:
-                result = 1.0
-            elif final_reward < 0:
-                result = 0.0
-
-            change = self.controller.compute_elo_rating_changes(
-                self.current_elo, result
-            )
-            self.change_current_elo(change)
-            self._stats_reporter.add_stat("Self-play/ELO", self.current_elo)
+        self.update_elo_ratings(trajectory)
 
     def advance(self) -> None:
         """
@@ -478,3 +459,31 @@ def subscribe_trajectory_queue(
                 parsed_behavior_id.brain_name
             ] = internal_trajectory_queue
             self.trainer.subscribe_trajectory_queue(internal_trajectory_queue)
+
+    def update_elo_ratings(self, trajectory: Trajectory) -> None:
+        """
+        Updates the ELO ratings based on the outcome of an episode.
+        This method encapsulates the ELO update logic that was previously
+        part of the _process_trajectory method.
+        
+        :param trajectory: Trajectory containing the episode outcome.
+        """
+        if (
+            trajectory.done_reached
+            and trajectory.all_group_dones_reached
+            and not trajectory.interrupted
+        ):
+            final_reward = (
+                trajectory.steps[-1].reward + trajectory.steps[-1].group_reward
+            )
+            result = 0.5
+            if final_reward > 0:
+                result = 1.0
+            elif final_reward < 0:
+                result = 0.0
+
+            change = self.controller.compute_elo_rating_changes(
+                self.current_elo, result
+            )
+            self.change_current_elo(change)
+            self._stats_reporter.add_stat("Self-play/ELO", self.current_elo)