remove x/y vel, smaller network

Project/Assets/ML-Agents/Examples/Tennis/Prefabs/TennisArea.prefab

     vectorActionSize: 03000000
     vectorActionDescriptions: []
     vectorActionSpaceType: 1
-  m_Model: {fileID: 11400000, guid: 13ca629afc16c42afb2d681a3d81ea6c, type: 3}
+  m_Model: {fileID: 11400000, guid: 02efff6cde0914e34bcc06eac03de48a, type: 3}
   m_InferenceDevice: 0
   m_BehaviorType: 0
   m_BehaviorName: Tennis
   invertX: 0
   score: 0
   myArea: {fileID: 1541947554534326}
-  angle: 0
-  energyPenalty: 0
 --- !u!114 &2449890524009497851
 MonoBehaviour:
   m_ObjectHideFlags: 0
   m_Name: 
   m_EditorClassIdentifier: 
   m_BrainParameters:
-    vectorObservationSize: 14
+    vectorObservationSize: 12
-  m_Model: {fileID: 11400000, guid: 13ca629afc16c42afb2d681a3d81ea6c, type: 3}
+  m_Model: {fileID: 11400000, guid: 02efff6cde0914e34bcc06eac03de48a, type: 3}
   m_InferenceDevice: 0
   m_BehaviorType: 0
   m_BehaviorName: Tennis
   invertX: 1
   score: 0
   myArea: {fileID: 1541947554534326}
-  angle: 0
-  energyPenalty: 0
 --- !u!114 &6598495797138489682
 MonoBehaviour:
   m_ObjectHideFlags: 0

Project/Assets/ML-Agents/Examples/Tennis/Scripts/TennisAgent.cs

 
         sensor.AddObservation(m_InvertMult * (opponent.transform.position.x - myArea.transform.position.x));
         sensor.AddObservation(opponent.transform.position.y - myArea.transform.position.y);
-        sensor.AddObservation(m_InvertMult * m_OpponentRb.velocity.x);
-        sensor.AddObservation(m_OpponentRb.velocity.y);
+        //sensor.AddObservation(m_InvertMult * m_OpponentRb.velocity.x);
+        //sensor.AddObservation(m_OpponentRb.velocity.y);
 
         sensor.AddObservation(m_InvertMult * gameObject.transform.rotation.z);
 

Project/Assets/ML-Agents/Examples/Tennis/Scripts/TennisArea.cs

     {
         m_BallRb.AddForce(down);
         var rgV = m_BallRb.velocity;
-        m_BallRb.velocity = new Vector3(Mathf.Clamp(rgV.x, -30f, 30f), Mathf.Min(rgV.y, 35f), rgV.z);
+        m_BallRb.velocity = new Vector3(Mathf.Clamp(rgV.x, -40f, 40f), Mathf.Min(rgV.y, 35f), rgV.z);
     }
 }

config/trainer_config.yaml

     learning_rate_schedule: constant
     batch_size: 2048
     buffer_size: 20480
-    hidden_units: 512
+    hidden_units: 256
     beta: 1.0e-2
     threaded: false
     time_horizon: 1000