Black format

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

         )
 
         self.optimizer = torch.optim.Adam(
-            params, lr=self.trainer_settings.hyperparameters.learning_rate, weight_decay=1e-6
+            params,
+            lr=self.trainer_settings.hyperparameters.learning_rate,
+            weight_decay=1e-6,
         )
         self.stats_name_to_update_name = {
             "Losses/Value Loss": "value_loss",
         value_loss = torch.mean(torch.stack(value_losses))
         return value_loss
 
-    def coma_regularizer_loss(self, values: Dict[str, torch.Tensor], baseline_values: Dict[str, torch.Tensor]):
+    def coma_regularizer_loss(
+        self, values: Dict[str, torch.Tensor], baseline_values: Dict[str, torch.Tensor]
+    ):
         reg_losses = []
         for name, head in values.items():
             reg_loss = torch.nn.functional.mse_loss(head, baseline_values[name])