fix whitespace and line breaks

ml-agents/mlagents/trainers/components/bc/module.py

         """
         A BC trainer that can be used inline with RL, especially for pretraining.
         :param policy: The policy of the learning model
-        :param policy_learning_rate: The initial Learning Rate of the policy. Used to set an appropriate learning rate for the pretrainer.
+        :param policy_learning_rate: The initial Learning Rate of the policy. Used to set an appropriate learning rate
+            for the pretrainer.
         :param default_batch_size: The default batch size to use if batch_size isn't provided.
         :param default_num_epoch: The default num_epoch to use if num_epoch isn't provided.
         :param strength: The proportion of learning rate used to update through BC.

ml-agents/mlagents/trainers/components/reward_signals/curiosity/signal.py

         :param gamma: The time discounting factor used for this reward.
         :param encoding_size: The size of the hidden encoding layer for the ICM
         :param learning_rate: The learning rate for the ICM.
-        :param num_epoch: The number of epochs to train over the training buffer for the ICM. 
+        :param num_epoch: The number of epochs to train over the training buffer for the ICM.
         """
         super().__init__(policy, strength, gamma)
         self.model = CuriosityModel(

ml-agents/mlagents/trainers/components/reward_signals/gail/signal.py

         :param learning_rate: The Learning Rate used during GAIL updates.
         :param samples_per_update: The maximum number of samples to update during GAIL updates.
         :param use_actions: Whether or not to use the actions for the discriminator.
-        :param use_vail: Whether or not to use a variational bottleneck for the discriminator. 
+        :param use_vail: Whether or not to use a variational bottleneck for the discriminator.
         See https://arxiv.org/abs/1810.00821.
         """
         super().__init__(policy, strength, gamma)
         cls, config_dict: Dict[str, Any], param_keys: List[str] = None
     ) -> None:
         """
-        Checks the config and throw an exception if a hyperparameter is missing. GAIL requires strength and gamma 
-        at minimum. 
+        Checks the config and throw an exception if a hyperparameter is missing. GAIL requires strength and gamma
+        at minimum.
         """
         param_keys = ["strength", "gamma", "demo_path"]
         super().check_config(config_dict, param_keys)

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

         Generates value estimates for bootstrapping.
         :param brain_info: BrainInfo to be used for bootstrapping.
         :param idx: Index in BrainInfo of agent.
-        :param done: Whether or not this is the last element of the episode, in which case we want the value estimate to be 0. 
+        :param done: Whether or not this is the last element of the episode, in which case the value estimate will be 0.
         :return: The value estimate dictionary with key being the name of the reward signal and the value the
         corresponding value estimate.
         """