remove old soccer image

Project/Assets/ML-Agents/Examples/Soccer/TFModels/SoccerTwos.nn.meta

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Project/Assets/ML-Agents/Examples/Soccer/Scripts/AgentSoccer.cs

     float m_KickPower;
     int m_PlayerIndex;
     public SoccerFieldArea area;
+    float m_BallTouch;
 
     [HideInInspector]
     public Rigidbody agentRb;
         var force = 2000f * m_KickPower;
         if (c.gameObject.CompareTag("ball"))
         {
+            AddReward(.2f * m_BallTouch);
             var dir = c.contacts[0].point - transform.position;
             dir = dir.normalized;
             c.gameObject.GetComponent<Rigidbody>().AddForce(dir * force);
     public override void OnEpisodeBegin()
     {
+
+        m_BallTouch = Academy.Instance.FloatProperties.GetPropertyWithDefault("ball_touch", 0);
         if (team == Team.Purple)
         {
             transform.rotation = Quaternion.Euler(0f, -90f, 0f);

config/trainer_config.yaml

         save_steps: 50000
         swap_steps: 50000
 
-Soccer:
+SoccerTwos:
-    max_steps: 5.0e7
+    max_steps: 1.0e8
     learning_rate_schedule: constant
     batch_size: 2048
     buffer_size: 20480
     self_play:
-        window: 10
-        play_against_current_self_ratio: 0.5
+        window: 100
+        play_against_current_self_ratio: 0.2
+
+SoccerFives:
+    normalize: false
+    max_steps: 2.0e8
+    learning_rate_schedule: constant
+    batch_size: 5096
+    buffer_size: 50960
+    hidden_units: 512
+    time_horizon: 1000
+    num_layers: 3
+    self_play:
+        window: 100
+        play_against_current_self_ratio: 0.2
+        save_steps: 100000
+        swap_steps: 100000
 
 CrawlerStatic:
     normalize: true

docs/Learning-Environment-Examples.md

         * Recommended Maximum: 250
 * Benchmark Mean Reward: 10
 
-## [Soccer Twos](https://youtu.be/Hg3nmYD3DjQ)
+## [Soccer](https://youtu.be/Hg3nmYD3DjQ)
-![SoccerTwos](images/soccer.png)
+![SoccerTwos](images/soccertwos.png) ![SoccerFives](images/soccerfives.png)
-* Set-up: Environment where four agents compete in a 2 vs 2 toy soccer game.
+* Set-up: Environment where agents compete in an n vs n toy soccer game.
-* Agents: The environment contains four agents, with the same
-  Behavior Parameters : Soccer.
+* Agents: The SoccerTwos environment contains four agents, with the same
+  Behavior Parameters : SoccerTwos. The SoccerFives environment contains ten agents, with the same
+  Behavior Parameters : SoccerFives.
 * Agent Reward Function (dependent):
     * +1 When ball enters opponent's goal.
     * -1 When ball enters team's goal.
     * Default: 9.81
     * Recommended minimum: 6
     * Recommended maximum: 20
+* Curriculum for SoccerFives: Since SoccerFives occurs on a larger field, it is more difficult for agents to
+experience terminal rewards.  So, we introduce a curriculum that initially rewards agents for touching the ball.
+These intermediate rewards help agents discover that 'kicking' the ball can lead to larger rewards. Please see
+our documentation for [Curriculum Learning](Training-Curriculum-Learning.md) for more details.
 
 ## Walker
 

Project/Assets/ML-Agents/Examples/Soccer/Scenes/SoccerFives.unity

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