reuse encoder and linear

Project/Assets/ML-Agents/Examples/3DBall/Scripts/Ball3DHardAgent.cs

         }
         else
         {
-            SetReward(0.1f);
+            // SetReward(0.1f);
+            SetReward(0.1f - (0.05f * actionZ * actionZ + 0.05f * actionX * actionX));
         }
     }
 

Project/ProjectSettings/EditorBuildSettings.asset

   m_ObjectHideFlags: 0
   serializedVersion: 2
   m_Scenes:
-  - enabled: 0
+  - enabled: 1
-  - enabled: 1
+  - enabled: 0
     path: Assets/ML-Agents/Examples/3DBall/Scenes/3DBall.unity
     guid: b9ac0cbf961bf4dacbfa0aa9c0d60aaa
   m_configObjects: {}

config/sac/3DBallHard.yaml

       learning_rate: 0.0003
       learning_rate_schedule: constant
       batch_size: 256
-      buffer_size: 50000
+      buffer_size: 500000
       buffer_init_steps: 0
       tau: 0.005
       steps_per_update: 10.0
     network_settings:
       normalize: true
-      hidden_units: 128
+      hidden_units: 64
       num_layers: 2
       vis_encode_type: simple
     reward_signals:

config/sac_transfer/3DBall.yaml

       separate_policy_train: true
       # separate_value_train: true
       separate_model_train: true
-      reuse_encoder: false
+      reuse_encoder: true
       in_epoch_alter: false
       in_batch_alter: true
       use_op_buffer: false

config/sac_transfer/3DBallHard.yaml

       separate_policy_train: true
       # separate_value_train: true
       separate_model_train: true
-      reuse_encoder: false
+      reuse_encoder: true
       in_epoch_alter: false
       in_batch_alter: true
       use_op_buffer: false

config/sac_transfer/3DBallHardTransfer.yaml

       # separate_value_net: true
       separate_policy_train: true
       # separate_value_train: true
-      reuse_encoder: false
+      reuse_encoder: true
       in_epoch_alter: false
       in_batch_alter: false
       use_op_buffer: false
       use_transfer: true
       load_model: true
       train_model: false
-      transfer_path: "results/ball-targ-linear/3DBall"
+      transfer_path: "results/ball-linear/3DBall"
     network_settings:
       normalize: true
       hidden_units: 64

ml-agents/mlagents/trainers/sac_transfer/network.py

             self.processed_vector_in, 
             vis_encode_type,
             encoder_layers=encoder_layers,
-            scope="target_enc",
+            scope="encoding",
             reuse=True
         )
         if separate_train: