use LinearEncoder in curiosity and clean up (#4444)

ml-agents/mlagents/trainers/torch/components/reward_providers/curiosity_reward_provider.py

 from mlagents_envs.base_env import BehaviorSpec
 from mlagents.trainers.torch.utils import ModelUtils
 from mlagents.trainers.torch.networks import NetworkBody
-from mlagents.trainers.torch.layers import linear_layer, Swish
+from mlagents.trainers.torch.layers import LinearEncoder, linear_layer
 from mlagents.trainers.settings import NetworkSettings, EncoderType
 
 
 
         self._action_flattener = ModelUtils.ActionFlattener(specs)
 
-        self.inverse_model_action_predition = torch.nn.Sequential(
-            linear_layer(2 * settings.encoding_size, 256),
-            Swish(),
+        self.inverse_model_action_prediction = torch.nn.Sequential(
+            LinearEncoder(2 * settings.encoding_size, 1, 256),
-            linear_layer(
-                settings.encoding_size + self._action_flattener.flattened_size, 256
+            LinearEncoder(
+                settings.encoding_size + self._action_flattener.flattened_size, 1, 256
-            Swish(),
             linear_layer(256, settings.encoding_size),
         )
 
         inverse_model_input = torch.cat(
             (self.get_current_state(mini_batch), self.get_next_state(mini_batch)), dim=1
         )
-        hidden = self.inverse_model_action_predition(inverse_model_input)
+        hidden = self.inverse_model_action_prediction(inverse_model_input)
         if self._policy_specs.is_action_continuous():
             return hidden
         else: