tennis curriculum

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

     public GameObject myArea;
     public float angle;
     public float scale;
+    float m_BallTouch;
 
     Text m_TextComponent;
     Rigidbody m_AgentRb;
         return action;
     }
 
+    void OnCollisionEnter(Collision c)
+    {
+        if (c.gameObject.CompareTag("ball"))
+        {
+            // 8f is for offset of ball to area
+            AddReward(.05f * m_BallTouch * (8f + ball.transform.position.y - myArea.transform.position.y));
+        }
+    }
+
+
+        m_BallTouch = Academy.Instance.FloatProperties.GetPropertyWithDefault("ball_touch", 0);
         m_InvertMult = invertX ? -1f : 1f;
 
         transform.position = new Vector3(-m_InvertMult * Random.Range(6f, 8f), -1.5f, -1.8f) + transform.parent.transform.position;

config/trainer_config.yaml

     time_horizon: 1000
     self_play:
         window: 10
-        play_against_latest_model_ratio: 0.2
+        play_against_latest_model_ratio: 0.5
         save_steps: 50000
         swap_steps: 50000
         team_change: 100000
-    max_steps: 5.0e7
+    max_steps: 3.0e7
     learning_rate_schedule: constant
     batch_size: 2048
     buffer_size: 20480
     self_play:
-        window: 100
-        play_against_latest_model_ratio: 0.2
+        window: 50
+        play_against_latest_model_ratio: 0.5
         save_steps: 50000
         swap_steps: 25000
         team_change: 200000
-    max_steps: 5.0e7
+    max_steps: 3.0e7
     learning_rate_schedule: constant
     batch_size: 2048
     buffer_size: 20480
     self_play:
-        window: 100
-        play_against_latest_model_ratio: 0.2
+        window: 50
+        play_against_latest_model_ratio: 0.5
         save_steps: 50000
         swap_steps: 100000
         team_change: 200000
-    max_steps: 3.0e7
+    max_steps: 5.0e7
     learning_rate_schedule: constant
     batch_size: 2048
     buffer_size: 20480
     self_play:
-        window: 100
-        play_against_latest_model_ratio: 0.2
+        window: 50
+        play_against_latest_model_ratio: 0.5
         save_steps: 50000
         swap_steps: 50000
         team_change: 200000

config/curricula/tennis.yaml

+Tennis:
+  measure: progress
+  thresholds: [0.05, 0.1]
+  min_lesson_length: 100
+  signal_smoothing: true
+  parameters:
+    ball_touch: [1.0, 0.5, 0.0]