Revert "action enc"

This reverts commit 9acb5f846a77a47d14694cb1271e2a370f395b6a.

config/ppo_transfer/3DBall.yaml

       encoder_layers: 1
       action_layers: 1
       policy_layers: 1
-      forward_layers: 0
+      forward_layers: 1
-      action_feature_size: 16
+      action_feature_size: 32
       reuse_encoder: true
       in_epoch_alter: false
       use_op_buffer: false

config/ppo_transfer/3DBallHard.yaml

       encoder_layers: 1
       action_layers: 1
       policy_layers: 1
-      forward_layers: 0
+      forward_layers: 1
-      action_feature_size: 16
+      action_feature_size: 32
       reuse_encoder: true
       in_epoch_alter: false
       use_op_buffer: false

config/ppo_transfer/3DBallHardTransfer.yaml

       encoder_layers: 1
       action_layers: 1
       policy_layers: 1
-      forward_layers: 0
+      forward_layers: 1
-      action_feature_size: 16
+      action_feature_size: 32
       reuse_encoder: true
       in_epoch_alter: false
       use_op_buffer: false
       train_model: false
       load_model: true
       train_action: false
-      load_action: true
+      load_action: false
       train_policy: true
       load_policy: false
       train_value: true

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

                 reuse_encoder,
             )
 
-            self.action_encoder = self._create_action_encoder(
-                self.current_action,
-                self.h_size,
-                self.action_feature_size,
-                action_layers,
-            )
+            self.action_encoder = self.current_action  # self._create_action_encoder(
+            #    self.current_action,
+            #    self.h_size,
+            #    self.action_feature_size,
+            #    action_layers,
+            # )
 
             if not reuse_encoder:
                 self.targ_encoder = tf.stop_gradient(self.targ_encoder)
             encoding_checkpoint = os.path.join(self.model_path, f"encoding.ckpt")
             encoding_saver.save(self.sess, encoding_checkpoint)
 
-            action_vars = tf.get_collection(
-               tf.GraphKeys.TRAINABLE_VARIABLES, "action_enc"
-            )
-            action_saver = tf.train.Saver(action_vars)
-            action_checkpoint = os.path.join(self.model_path, f"action_enc.ckpt")
-            action_saver.save(self.sess, action_checkpoint)
+            # action_vars = tf.get_collection(
+            #    tf.GraphKeys.TRAINABLE_VARIABLES, "action_enc"
+            # )
+            # action_saver = tf.train.Saver(action_vars)
+            # action_checkpoint = os.path.join(self.model_path, f"action_enc.ckpt")
+            # action_saver.save(self.sess, action_checkpoint)
 
             latent_vars = tf.get_collection(
                 tf.GraphKeys.TRAINABLE_VARIABLES, "encoding/latent"
         :param encoded_state: Tensor corresponding to encoded current state.
         :param encoded_next_state: Tensor corresponding to encoded next state.
         """
-        if not self.transfer:
-            encoded_state = tf.stop_gradient(encoded_state)
-        
+        if not self.transfer:
+            hidden = tf.stop_gradient(hidden)
+
         for i in range(forward_layers):
             hidden = tf.layers.dense(
                 hidden,
         forward_layers: int,
         separate_train: bool = False,
     ):
-        if not self.transfer:
-            encoded_state = tf.stop_gradient(encoded_state)
+
+        if not self.transfer:
+            hidden = tf.stop_gradient(hidden)
 
         for i in range(forward_layers):
             hidden = tf.layers.dense(