current_best_ratio -> latest_model_ratio

config/trainer_config.yaml

     time_horizon: 1000
     self_play:
         window: 10
-        play_against_current_best_ratio: 0.5
+        play_against_latest_model_ratio: 0.5
         save_steps: 50000
         swap_steps: 50000
         team_change: 100000
     num_layers: 2
     self_play:
         window: 10
-        play_against_current_best_ratio: 0.5
+        play_against_latest_model_ratio: 0.5
         save_steps: 50000
         swap_steps: 50000
         team_change: 100000

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

 
         self_play_parameters = trainer_parameters["self_play"]
         self.window = self_play_parameters.get("window", 10)
-        self.play_against_current_best_ratio = self_play_parameters.get(
-            "play_against_current_best_ratio", 0.5
+        self.play_against_latest_model_ratio = self_play_parameters.get(
+            "play_against_latest_model_ratio", 0.5
         )
         self.steps_between_save = self_play_parameters.get("save_steps", 20000)
         self.steps_between_swap = self_play_parameters.get("swap_steps", 20000)
         for team_id in self._team_to_name_to_policy_queue:
             if team_id == self._learning_team:
                 continue
-            elif np.random.uniform() < (1 - self.play_against_current_best_ratio):
+            elif np.random.uniform() < (1 - self.play_against_latest_model_ratio):
                 x = np.random.randint(len(self.policy_snapshots))
                 snapshot = self.policy_snapshots[x]
             else:

ml-agents/mlagents/trainers/tests/test_simple_rl.py

     override_vals = {
         "max_steps": 2500,
         "self_play": {
-            "play_against_current_best_ratio": 1.0,
+            "play_against_latest_model_ratio": 1.0,
             "save_steps": 2000,
             "swap_steps": 2000,
         },
     override_vals = {
         "max_steps": 2500,
         "self_play": {
-            "play_against_current_best_ratio": 1.0,
+            "play_against_latest_model_ratio": 1.0,
             "save_steps": 2000,
             "swap_steps": 4000,
         },
     override_vals = {
         "max_steps": 2000,
         "self_play": {
-            "play_against_current_best_ratio": 1.0,
+            "play_against_latest_model_ratio": 1.0,
             "save_steps": 5000,
             "swap_steps": 5000,
             "team_change": 2000,
     override_vals = {
         "max_steps": 2000,
         "self_play": {
-            "play_against_current_best_ratio": 0.0,
+            "play_against_latest_model_ratio": 0.0,
             "save_steps": 5000,
             "swap_steps": 5000,
             "team_change": 2000,