Rename resample to reparameterize

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

         is_training: bool,
         load: bool,
         tanh_squash: bool = False,
-        resample: bool = False,
+        reparameterize: bool = False,
         condition_sigma_on_obs: bool = True,
         create_tf_graph: bool = True,
     ):
         :param is_training: Whether the model should be trained.
         :param load: Whether a pre-trained model will be loaded or a new one created.
         :param tanh_squash: Whether to use a tanh function on the continuous output, or a clipped output.
-        :param resample: Whether we are using the resampling trick to update the policy in continuous output.
+        :param reparameterize: Whether we are using the resampling trick to update the policy in continuous output.
         """
         super().__init__(seed, brain, trainer_params, load)
         self.grads = None
             trainer_params.get("vis_encode_type", "simple")
         )
         self.tanh_squash = tanh_squash
-        self.resample = resample
+        self.reparameterize = reparameterize
         self.condition_sigma_on_obs = condition_sigma_on_obs
         self.trainable_variables: List[tf.Variable] = []
 
                     self.num_layers,
                     self.vis_encode_type,
                     self.tanh_squash,
-                    self.resample,
+                    self.reparameterize,
                     self.condition_sigma_on_obs,
                 )
             else:
         num_layers: int,
         vis_encode_type: EncoderType,
         tanh_squash: bool = False,
-        resample: bool = False,
+        reparameterize: bool = False,
         condition_sigma_on_obs: bool = True,
     ) -> None:
         """
         :param vis_encode_type: Type of visual encoder to use if visual input.
         :param tanh_squash: Whether to use a tanh function, or a clipped output.
-        :param resample: Whether we are using the resampling trick to update the policy.
+        :param reparameterize: Whether we are using the resampling trick to update the policy.
         """
         with tf.variable_scope("policy"):
             hidden_stream = ModelUtils.create_observation_streams(
             sampled_policy = mu + sigma * epsilon
 
             # Stop gradient if we're not doing the resampling trick
-            if not resample:
+            if not reparameterize:
                 sampled_policy_probs = tf.stop_gradient(sampled_policy)
             else:
                 sampled_policy_probs = sampled_policy

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

             self.is_training,
             self.load,
             tanh_squash=True,
-            resample=True,
+            reparameterize=True,
             create_tf_graph=False,
         )
         for _reward_signal in policy.reward_signals.keys():