Clean up nn_policy

ml-agents/mlagents/trainers/common/nn_policy.py

         :param resample: Whether we are using the resampling trick to update the policy in continuous output.
         """
         super().__init__(seed, brain, trainer_params, load)
-
-        self.stats_name_to_update_name = {
-            "Losses/Value Loss": "value_loss",
-            "Losses/Policy Loss": "policy_loss",
-        }
-
         self.tf_optimizer: Optional[tf.train.Optimizer] = None
         self.grads = None
         self.update_batch: Optional[tf.Operation] = None

ml-agents/mlagents/trainers/tf_policy.py

 class TFPolicy(Policy):
     """
     Contains a learning model, and the necessary
-    functions to interact with it to perform evaluate and updating.
+    functions to save/load models and create the input placeholders.
     """
 
     possible_output_nodes = [