StrikerVsGoalie and SoccerTwos env improvements (#3699)

Project/Assets/ML-Agents/Examples/Soccer/Prefabs/SoccerFieldTwos.prefab

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

 using UnityEngine;
 using MLAgents;
 using MLAgents.Policies;
+using MLAgents.SideChannels;
 
 public class AgentSoccer : Agent
 {
         Purple = 1
     }
 
+    public enum Position
+    {
+        Striker,
+        Goalie,
+        Generic
+    }
+
+    // The coefficient for the reward for colliding with a ball. Set using curriculum.
+    float m_BallTouch;
+    public Position position;
+
+    const float k_Power = 2000f;
+    float m_Existential;
+    float m_LateralSpeed;
+    float m_ForwardSpeed;
+
+    [HideInInspector]
+    public float timePenalty = 0;
 
     [HideInInspector]
     public Rigidbody agentRb;
 
     public override void Initialize()
     {
+        m_Existential = 1f / maxStep;
         m_BehaviorParameters = gameObject.GetComponent<BehaviorParameters>();
         if (m_BehaviorParameters.TeamId == (int)Team.Blue)
         {
             team = Team.Purple;
             m_Transform = new Vector3(transform.position.x + 4f, .5f, transform.position.z);
         }
+        if (position == Position.Goalie)
+        {
+            m_LateralSpeed = 1.0f;
+            m_ForwardSpeed = 1.0f;
+        }
+        else if (position == Position.Striker)
+        {
+            m_LateralSpeed = 0.3f;
+            m_ForwardSpeed = 1.3f;
+        }
+        else 
+        {
+            m_LateralSpeed = 0.3f;
+            m_ForwardSpeed = 1.0f;
+        }
         m_SoccerSettings = FindObjectOfType<SoccerSettings>();
         agentRb = GetComponent<Rigidbody>();
         agentRb.maxAngularVelocity = 500;
         switch (forwardAxis)
         {
             case 1:
-                dirToGo = transform.forward * 1f;
+                dirToGo = transform.forward * m_ForwardSpeed;
-                dirToGo = transform.forward * -1f;
+                dirToGo = transform.forward * -m_ForwardSpeed;
                 break;
         }
 
-                dirToGo = transform.right * 0.3f;
+                dirToGo = transform.right * m_LateralSpeed;
-                dirToGo = transform.right * -0.3f;
+                dirToGo = transform.right * -m_LateralSpeed;
                 break;
         }
 
 
     public override void OnActionReceived(float[] vectorAction)
     {
-        // Existential penalty for strikers.
-        AddReward(-1f / 3000f);
+
+        if (position == Position.Goalie)
+        {
+            // Existential bonus for Goalies.
+            AddReward(m_Existential);
+        }
+        else if (position == Position.Striker)
+        {
+            // Existential penalty for Strikers
+            AddReward(-m_Existential);
+        }
+        else
+        {
+            // Existential penalty cumulant for Generic
+            timePenalty -= m_Existential;
+        }
         MoveAgent(vectorAction);
     }
 
     /// </summary>
     void OnCollisionEnter(Collision c)
     {
-        var force = 2000f * m_KickPower;
+        var force = k_Power * m_KickPower;
+        if (position == Position.Goalie)
+        {
+            force = k_Power;
+        }
+            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()
     {
+
+        timePenalty = 0;
+        m_BallTouch = SideChannelUtils.GetSideChannel<FloatPropertiesChannel>().GetPropertyWithDefault("ball_touch", 0);
         if (team == Team.Purple)
         {
             transform.rotation = Quaternion.Euler(0f, -90f, 0f);

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

         {
             if (ps.agentScript.team == scoredTeam)
             {
-                ps.agentScript.AddReward(1);
+                ps.agentScript.AddReward(1 + ps.agentScript.timePenalty);
             }
             else
             {
         }
     }
 
-    public Vector3 GetBallSpawnPosition()
-    {
-        var randomSpawnPos = ground.transform.position +
-            new Vector3(0f, 0f, 0f);
-        randomSpawnPos.y = ground.transform.position.y + .5f;
-        return randomSpawnPos;
-    }
-
-        ball.transform.position = GetBallSpawnPosition();
+        ball.transform.position = ballStartingPos;
         ballRb.velocity = Vector3.zero;
         ballRb.angularVelocity = Vector3.zero;
 

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

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com.unity.ml-agents/CHANGELOG.md

   communication between Unity and the Python process.
 - The obsolete `Agent` methods `GiveModel`, `Done`, `InitializeAgent`,
   `AgentAction` and `AgentReset` have been removed.
+- The GhostTrainer has been extended to support asymmetric games and the asymmetric example environment Strikers Vs. Goalie has been added.
 
 ### Minor Changes
 

config/trainer_config.yaml

         swap_steps: 50000
         team_change: 100000
 
-Soccer:
+Goalie:
+    normalize: false
+    max_steps: 5.0e7
+    learning_rate_schedule: constant
+    batch_size: 2048
+    buffer_size: 20480
+    hidden_units: 512
+    time_horizon: 1000
+    num_layers: 2
+    self_play:
+        window: 10
+        play_against_latest_model_ratio: 0.5
+        save_steps: 50000
+        swap_steps: 25000
+        team_change: 200000
+
+Striker:
+    normalize: false
+    max_steps: 5.0e7
+    learning_rate_schedule: constant
+    batch_size: 2048
+    buffer_size: 20480
+    hidden_units: 512
+    time_horizon: 1000
+    num_layers: 2
+    self_play:
+        window: 10
+        play_against_latest_model_ratio: 0.5
+        save_steps: 50000
+        swap_steps: 100000
+        team_change: 200000
+
+SoccerTwos:
     normalize: false
     max_steps: 5.0e7
     learning_rate_schedule: constant
         play_against_latest_model_ratio: 0.5
         save_steps: 50000
         swap_steps: 50000
-        team_change: 100000
+        team_change: 200000
 
 CrawlerStatic:
     normalize: true

docs/Learning-Environment-Examples.md

 
 - Set-up: Environment where four agents compete in a 2 vs 2 toy soccer game.
 - Goal:
-  - Get the ball into the opponent's goal while preventing the ball from
-    entering own goal.
-- Agents: The environment contains four agents, with the same Behavior
-  Parameters : Soccer.
+  - Get the ball into the opponent's goal while preventing the ball from entering own goal.
+- Agents: The environment contains four agents, with the same
+  Behavior Parameters : SoccerTwos.
-  - +1 When ball enters opponent's goal.
-  - -1 When ball enters team's goal.
-  - -0.001 Existential penalty.
+    - (1 - `accumulated time penalty`) When ball enters opponent's goal `accumulated time penalty` is incremented by
+    (1 / `maxStep`) every fixed update and is reset to 0 at the beginning of an episode.
+    - -1 When ball enters team's goal.
-  - Vector Observation space: 336 corresponding to 11 ray-casts forward
-    distributed over 120 degrees (264) and 3 ray-casts backward distributed over
-    90 degrees each detecting 6 possible object types, along with the object's
-    distance. The forward ray-casts contribute 264 state dimensions and backward
-    72 state dimensions.
-  - Vector Action space: (Discrete) Three branched actions corresponding to
-    forward, backward, sideways movement, as well as rotation.
+  - Vector Observation space: 336 corresponding to 11 ray-casts forward distributed over 120 degrees
+    and 3 ray-casts backward distributed over 90 degrees each detecting 6 possible object types, along with the object's distance.
+    The forward ray-casts contribute 264 state dimensions and backward 72 state dimensions over three observation stacks.
+  - Vector Action space: (Discrete) Three branched actions corresponding to forward, backward, sideways movement,
+      as well as rotation.
-  - ball_scale: Specifies the scale of the ball in the 3 dimensions (equal
-    across the three dimensions)
+  - ball_scale: Specifies the scale of the ball in the 3 dimensions (equal across the three dimensions)
     - Default: 7.5
     - Recommended minimum: 4
     - Recommended maximum: 10
     - Recommended maximum: 20
+
+# Strikers Vs. Goalie
+
+![StrikersVsGoalie](images/strikersvsgoalie.png)
+
+- Set-up: Environment where two agents compete in a 2 vs 1 soccer variant.
+- Goal:
+  - Striker: Get the ball into the opponent's goal.
+  - Goalie: Keep the ball out of the goal.
+- Agents: The environment contains three agents. Two Strikers and one Goalie.
+  Behavior Parameters : Striker, Goalie.
+- Striker Agent Reward Function (dependent):
+    - +1 When ball enters opponent's goal.
+    - -0.001 Existential penalty.
+- Goalie Agent Reward Function (dependent):
+    - -1 When ball enters goal.
+    - 0.001 Existential bonus.
+- Behavior Parameters:
+  - Striker Vector Observation space: 294 corresponding to 11 ray-casts forward distributed over 120 degrees
+    and 3 ray-casts backward distributed over 90 degrees each detecting 5 possible object types, along with the object's distance.
+    The forward ray-casts contribute 231 state dimensions and backward 63 state dimensions over three observation stacks.
+  - Striker Vector Action space: (Discrete) Three branched actions corresponding to forward, backward, sideways movement,
+      as well as rotation.
+  - Goalie Vector Observation space: 738 corresponding to 41 ray-casts distributed over 360 degrees
+    each detecting 4 possible object types, along with the object's distance and 3 observation stacks.
+  - Goalie Vector Action space: (Discrete) Three branched actions corresponding to forward, backward, sideways movement,
+      as well as rotation.
+  - Visual Observations: None
+- Float Properties: Two
+  - ball_scale: Specifies the scale of the ball in the 3 dimensions (equal across the three dimensions)
+    - Default: 7.5
+    - Recommended minimum: 4
+    - Recommended maximum: 10
+  - gravity: Magnitude of the gravity
+    - Default: 9.81
+    - Recommended minimum: 6
+    - Recommended maximum: 20
+
 
 ## Walker
 

docs/Training-Self-Play.md

 A symmetric game is one in which opposing agents are equal in form, function and objective. Examples of symmetric games
 are our Tennis and Soccer example environments. In reinforcement learning, this means both agents have the same observation and
 action spaces and learn from the same reward function and so *they can share the same policy*. In asymmetric games,
-this is not the case. An example of an asymmetric games are Hide and Seek. Agents in these
+this is not the case. An example of an asymmetric game is our Strikers Vs Goalie example environment. Agents in these
 types of games do not always have the same observation or action spaces and so sharing policy networks is not
 necessarily ideal.
 
 Note, in asymmetric games, the agents must have both different Behavior Names *and* different team IDs! Then, specify the trainer configuration
 for each Behavior Name in your scene as you would normally, and remember to include the self-play hyperparameter hierarchy!
 
-For examples of how to use this feature, you can see the trainer configurations and agent prefabs for our Tennis and Soccer environments.
-Tennis and Soccer provide examples of symmetric games. To train an asymmetric game, specify trainer configurations for each of your behavior names
-and include the self-play hyperparameter hierarchy in both.
+For examples of how to use this feature, you can see the trainer configurations and agent prefabs for our Tennis, Soccer, and
+Strikers Vs Goalie environments.
+Tennis and Soccer provide examples of symmetric games and Strikers Vs Goalie provides an example of an asymmetric game.
 
 
 ## Best Practices Training with Self-Play
 ### Reward Signals
 
 We make the assumption that the final reward in a trajectory corresponds to the outcome of an episode.
-A final reward of +1 indicates winning, -1 indicates losing and 0 indicates a draw.
-The ELO calculation (discussed below) depends on this final reward being either +1, 0, -1.
+A final reward greater than 0 indicates winning, less than 0 indicates losing and 0 indicates a draw.
+The final reward determines the result of an episode (win, loss, or draw) in the ELO calculation.
+In problems that are too challenging to be solved by sparse rewards, it may be necessary to provide intermediate rewards to encourage useful instrumental behaviors.
+For example, it may be difficult for a soccer agent to learn that kicking a ball into the net receives a reward because this sequence has a low probability
+of occurring randomly.  However, it will have a higher probability of occurring if the agent learns generally that kicking the ball has utility. So, we may be able
+to speed up training by giving the agent intermediate reward for kicking the ball. However, we must be careful that the agent doesn't learn to undermine
+its original objective of scoring goals e.g. if it scores a goal, the episode ends and it can no longer receive reward for kicking the ball. The behavior
+that receives the most reward may be to keep the ball out of the net and to kick it indefinitely! To address this, we suggest
+using a curriculum that allows the agents to learn the necessary intermediate behavior (i.e. colliding with a ball) and then
+decays this reward signal to allow training on just the rewards of winning and losing. Please see our documentation on
+how to use curriculum learning [here](./Training-Curriculum-Learning.md) and our SoccerTwos example environment.
+
 ### Save Steps
 
 The `save_steps` parameter corresponds to the number of *trainer steps* between snapshots.  For example, if `save_steps=10000` then a snapshot of the current policy will be saved every `10000` trainer steps. Note, trainer steps are counted per agent. For more information, please see the [migration doc](Migrating.md) after v0.13.
 
 The `swap_steps` parameter corresponds to the number of *ghost steps* (not trainer steps) between swapping the opponents policy with a different snapshot.
 A 'ghost step' refers to a step taken by an agent *that is following a fixed policy and not learning*. The reason for this distinction is that in asymmetric games,
-we may have teams with an unequal number of agents e.g. a 2v1 scenario. The team with two agents collects
+we may have teams with an unequal number of agents e.g. a 2v1 scenario like our Strikers Vs Goalie example environment. The team with two agents collects
 twice as many agent steps per environment step as the team with one agent.  Thus, these two values will need to be distinct to ensure that the same number
 of trainer steps corresponds to the same number of opponent swaps for each team. The formula for `swap_steps` if
 a user desires `x` swaps of a team with `num_agents` agents against an opponent team with `num_opponent_agents`

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config/curricula/soccer.yaml

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+  parameters:
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