Merge branch 'development-0.3' into dev-api-doc-decision

docs/Learning-Environment-Design-Brains.md

 # Brains
 
-The Brain encapsulates the decision making process. Brain objects must be children of the Academy in the Unity scene hierarchy. Every Agent must be assigned a Brain, but you can use the same Brain with more than one Agent. 
+The Brain encapsulates the decision making process. Brain objects must be children of the Academy in the Unity scene hierarchy. Every Agent must be assigned a Brain, but you can use the same Brain with more than one Agent. You can also create several Brains, attach each of the Brain to one or more than one Agent.  
-Use the Brain class directly, rather than a subclass. Brain behavior is determined by the brain type. During training, set your agent's brain type to **External**. To use the trained model, import the model file into the Unity project and change the brain type to **Internal**. You can extend the CoreBrain class to create different brain types if the four built-in types don't do what you need.
+Use the Brain class directly, rather than a subclass. Brain behavior is determined by the brain type. During training, set your agent's brain type to **External**. You can have multiple brains set to **External** and train them simultaneously. To use the trained model, import the model file into the Unity project and change the brain type to **Internal**. You can extend the CoreBrain class to create different brain types if the four built-in types don't do what you need.
 
 The Brain class has several important properties that you can set using the Inspector window. These properties must be appropriate for the agents using the brain. For example, the `Vector Observation Space Size` property must match the length of the feature vector created by an agent exactly. See [Agents](Learning-Environment-Design-Agents.md) for information about creating agents and setting up a Brain instance correctly.
 

docs/Learning-Environment-Examples.md

 
 * Set-up: A platforming environment where the agent can jump over a wall.
 * Goal: The agent must use the block to scale the wall and reach the goal.
-* Agents: The environment contains one agent linked to a single brain.
+* Agents: The environment contains one agent linked to two different brains. The brain the agent is linked to changes depending on the height of the wall.
-    * -0.01 for every step.
+    * -0.0005 for every step.
-    * Vector Action space: (Discrete) Size of 6, corresponding to movement in cardinal directions, jumping, and no movement.
+    * Vector Action space: (Discrete) Size of 74, corresponding to 14 raycasts each detecting 4 possible objects. plus the global position of the agent and whether or not the agent is grounded.
-* Reset Parameters: One, corresponding to number of steps in training. Used to adjust size of the wall for Curriculum Learning.
+* Reset Parameters: 4, corresponding to the height of the possible walls.
 
 ## Reacher
 

docs/ML-Agents-Overview.md

 must react to events or games where agents can take actions of variable 
 duration. Switching between decision taking at every step and 
 on-demand-decision is one button click away. You can learn more about the 
-on-demand-decision feature [here](Feature-On-Demand-Decision.md).
+on-demand-decision feature [here](Feature-On-Demand-Decisions.md).
 
 * **Memory-enhanced Agents** - In some scenarios, agents must learn to 
 remember the past in order to take the 

docs/Readme.md

 ## Help
  * [ML-Agents Glossary](Glossary.md)
  * [Limitations & Common Issues](Limitations-and-Common-Issues.md)
+ * [Migrating to ML-Agents v0.3](Migrating-v0.3.md)
  
 ## C# API and Components
  * Academy

docs/Using-Docker.md

            --mount type=bind,source="$(pwd)"/unity-volume,target=/unity-volume \
            <image-name>:latest <environment-name> \
            --docker-target-name=unity-volume \
-           --train --run-id=<run-id>
+           --train \
+           --run-id=<run-id>
-- `<container-name>` is used to identify the container (in case you want to interrupt and terminate it). This is optional and Docker will set a random string if this is not set. _Note that this must be unique for every run of a Docker Image_
+- `<container-name>` is used to identify the container (in case you want to interrupt and terminate it). This is optional and Docker will generate a random name if this is not set. _Note that this must be unique for every run of a Docker image._
 - `<image-name>` and `<environment-name>`: References the image and environment names, respectively.
 - `source`: Reference to the path in your host OS where you will store the Unity executable. 
 - `target`: Tells Docker to mount the `source` path as a disk with this name. 
            --mount type=bind,source="$(pwd)"/unity-volume,target=/unity-volume \
            balance.ball.v0.1:latest 3Dball \
            --docker-target-name=unity-volume \
-           --train --run-id=3dball_first_trial
+           --train \
+           --run-id=3dball_first_trial
 ```
 
 For more detail on Docker mounts, check out [these](https://docs.docker.com/storage/bind-mounts/) docs from Docker.
 
-
-If you are satisfied with the progress being made by the algorithm in training, you can stop the docker container, while saving state using the following :
+If you are satisfied with the training progress, you can stop the Docker container while saving state using the following command:
-`<container-name>` is the name of the container that you set during the `docker run`. If you didn't set the container name there, you can find the automatically generated identifier by running `docker container ls`.
+`<container-name>` is the name of the container specified in the earlier `docker run` command. If you didn't specify one, you can find the randomly generated identifier by running `docker container ls`.

python/tests/test_bc.py

       "brainNames": ["RealFakeBrain"],
       "externalBrainNames": ["RealFakeBrain"],
       "logPath":"RealFakePath",
-      "apiNumber":"API-2",
+      "apiNumber":"API-3",
       "brainParameters": [{
           "vectorObservationSize": 3,
           "numStackedVectorObservations": 2,
       "brainNames": ["RealFakeBrain"],
       "externalBrainNames": ["RealFakeBrain"],
       "logPath":"RealFakePath",
-      "apiNumber":"API-2",
+      "apiNumber":"API-3",
       "brainParameters": [{
           "vectorObservationSize": 3,
           "numStackedVectorObservations": 2,

python/tests/test_ppo.py

       "brainNames": ["RealFakeBrain"],
       "externalBrainNames": ["RealFakeBrain"],
       "logPath":"RealFakePath",
-      "apiNumber":"API-2",
+      "apiNumber":"API-3",
       "brainParameters": [{
           "vectorObservationSize": 3,
           "numStackedVectorObservations": 2,
       "brainNames": ["RealFakeBrain"],
       "externalBrainNames": ["RealFakeBrain"],
       "logPath":"RealFakePath",
-      "apiNumber":"API-2",
+      "apiNumber":"API-3",
       "brainParameters": [{
           "vectorObservationSize": 3,
           "numStackedVectorObservations": 2,

python/tests/test_unityagents.py

   "brainNames": ["RealFakeBrain"],
   "externalBrainNames": ["RealFakeBrain"],
   "logPath":"RealFakePath",
-  "apiNumber":"API-2",
+  "apiNumber":"API-3",
   "brainParameters": [{
       "vectorObservationSize": 3,
       "numStackedVectorObservations": 2,

python/tests/test_unitytrainers.py

   "brainNames": ["RealFakeBrain"],
   "externalBrainNames": ["RealFakeBrain"],
   "logPath":"RealFakePath",
-  "apiNumber":"API-2",
+  "apiNumber":"API-3",
   "brainParameters": [{
       "vectorObservationSize": 3,
       "numStackedVectorObservations" : 2,

python/trainer_config.yaml

     summary_freq: 1000
     use_recurrent: false
 
+BananaBrain:
+    normalize: false
+    batch_size: 1024
+    beta: 5.0e-3
+    buffer_size: 10240
+
-    max_steps: 2.0e5
-    batch_size: 1048
-    buffer_size: 10240
+    max_steps: 5.0e4
+    batch_size: 128
+    buffer_size: 2048
-    hidden_units: 128
+    hidden_units: 256
+
+SmallWallBrain: 
+    max_steps: 2.0e5
+    batch_size: 128
+    buffer_size: 2048
+    beta: 5.0e-3
+    hidden_units: 256
+    summary_freq: 2000
+    time_horizon: 128
+    num_layers: 2
+    normalize: false
+
+BigWallBrain: 
+    max_steps: 2.0e5
+    batch_size: 128
+    buffer_size: 2048
+    beta: 5.0e-3
+    hidden_units: 256
+    summary_freq: 2000
+    time_horizon: 128
+    num_layers: 2
+    normalize: false
 
 StrikerBrain:
     max_steps: 1.0e5
     batch_size: 32
     buffer_size: 320
 
-HallwayBrainDC:
-    num_layers: 3
-    hidden_units: 256
-    beta: 1.0e-2
-    gamma: 0.99
-    num_epoch: 3
-    buffer_size: 512
-    batch_size: 64
-    max_steps: 5.0e5
-    summary_freq: 1000
-    time_horizon: 64
-
-BrainWallJumpDC:
+HallwayBrain:
+    memory_size: 256
-    buffer_size: 16
-    batch_size: 2
-    max_steps: 5.0e5
-    summary_freq: 1000
-    time_horizon: 64
-
-HallwayBrainDCLSTM:
-    use_recurrent: true
-    sequence_length: 64
-    num_layers: 2
-    hidden_units: 128
-    beta: 1.0e-2
-    gamma: 0.99
-    num_epoch: 3
-    buffer_size: 16
-    batch_size: 2
+    buffer_size: 1024
+    batch_size: 128
     max_steps: 5.0e5
     summary_freq: 1000
     time_horizon: 64

python/unityagents/environment.py

         atexit.register(self.close)
         self.port = base_port + worker_id
         self._buffer_size = 12000
-        self._python_api = "API-2"
+        self._version_ = "API-3"
         self._loaded = False
         self._open_socket = False
 
                     .format(str(file_name)))
 
             if "apiNumber" not in p:
-                self._unity_api = "API-1"
+                self._unity_version = "API-1"
-                self._unity_api = p["apiNumber"]
-            if self._unity_api != self._python_api:
+                self._unity_version = p["apiNumber"]
+            if self._unity_version != self._version_:
-                    "of ML-Agents.".format(self._python_api, self._unity_api))
+                    "of ML-Agents.".format(self._version_, self._unity_version))
             self._data = {}
             self._global_done = None
             self._academy_name = p["AcademyName"]

unity-environment/Assets/ML-Agents/Examples/Banana/Prefabs/BANANA.prefab

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   - component: {fileID: 114475702868436998}
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-  m_Name: BANANA
+  m_Name: Banana
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   m_Icon: {fileID: 0}
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+  myArea: {fileID: 0}

unity-environment/Assets/ML-Agents/Examples/Banana/Prefabs/StudentAgent.prefab

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   m_StaticBatchInfo:
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   m_StaticBatchInfo:
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+  m_RenderingLayerMask: 4294967295
   m_Materials:
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   m_StaticBatchInfo:
   m_MotionVectors: 1
   m_LightProbeUsage: 1
   m_ReflectionProbeUsage: 1
+  m_RenderingLayerMask: 4294967295
   m_Materials:
   - {fileID: 2100000, guid: 866f7a84824d141dbbe50dd1893207d9, type: 2}
   m_StaticBatchInfo:
   m_Name: 
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   brain: {fileID: 0}
-  observations: []
-  maxStep: 0
-  resetOnDone: 1
-  state: []
-  stackedStates: []
-  reward: 0
-  done: 0
-  maxStepReached: 0
-  value: 0
-  CumulativeReward: 0
-  stepCounter: 0
-  agentStoredAction: []
-  memory: []
-  id: 0
+  agentParameters:
+    agentCameras: []
+    maxStep: 0
+    resetOnDone: 1
+    onDemandDecision: 0
+    numberOfActionsBetweenDecisions: 1
-  xForce: 100
-  yForce: 300
-  zForce: 100
+  moveSpeed: 2
-  myLazer: {fileID: 1711242980656446}
+  myLaser: {fileID: 0}
   contribute: 1

unity-environment/Assets/ML-Agents/Examples/Banana/Prefabs/TeacherAgent.prefab

   - component: {fileID: 54684485300203016}
   - component: {fileID: 114899212543667072}
   m_Layer: 0
-  m_Name: ExpertAgent
+  m_Name: TeacherAgent
   m_TagString: agent
   m_Icon: {fileID: 0}
   m_NavMeshLayer: 0
   m_MotionVectors: 1
   m_LightProbeUsage: 1
   m_ReflectionProbeUsage: 1
+  m_RenderingLayerMask: 4294967295
   m_Materials:
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   m_StaticBatchInfo:
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   m_ReflectionProbeUsage: 1
+  m_RenderingLayerMask: 4294967295
   m_Materials:
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   m_StaticBatchInfo:
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+  m_RenderingLayerMask: 4294967295
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   m_MotionVectors: 1
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   m_ReflectionProbeUsage: 1
+  m_RenderingLayerMask: 4294967295
   m_Materials:
   - {fileID: 2100000, guid: 88b9ae7af2c1748a0a1f63407587a601, type: 2}
   m_StaticBatchInfo:
   m_MotionVectors: 1
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   m_ReflectionProbeUsage: 1
+  m_RenderingLayerMask: 4294967295
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   m_StaticBatchInfo:
   m_MotionVectors: 1
   m_LightProbeUsage: 1
   m_ReflectionProbeUsage: 1
+  m_RenderingLayerMask: 4294967295
   m_Materials:
   - {fileID: 2100000, guid: 866f7a84824d141dbbe50dd1893207d9, type: 2}
   m_StaticBatchInfo:
   m_MotionVectors: 1
   m_LightProbeUsage: 1
   m_ReflectionProbeUsage: 1
+  m_RenderingLayerMask: 4294967295
   m_Materials:
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   m_StaticBatchInfo:
   m_MotionVectors: 1
   m_LightProbeUsage: 1
   m_ReflectionProbeUsage: 1
+  m_RenderingLayerMask: 4294967295
   m_Materials:
   - {fileID: 2100000, guid: 262d8cbc02b104990841408098431457, type: 2}
   m_StaticBatchInfo:
   m_MotionVectors: 1
   m_LightProbeUsage: 1
   m_ReflectionProbeUsage: 1
+  m_RenderingLayerMask: 4294967295
   m_Materials:
   - {fileID: 2100000, guid: 866f7a84824d141dbbe50dd1893207d9, type: 2}
   m_StaticBatchInfo:
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   brain: {fileID: 0}
-  observations: []
-  maxStep: 0
-  resetOnDone: 1
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-  stackedStates: []
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-  maxStepReached: 0
-  value: 0
-  CumulativeReward: 0
-  stepCounter: 0
-  agentStoredAction: []
-  memory: []
-  id: 0
+  agentParameters:
+    agentCameras: []
+    maxStep: 0
+    resetOnDone: 1
+    onDemandDecision: 0
+    numberOfActionsBetweenDecisions: 1
-  xForce: 100
-  yForce: 300
-  zForce: 100
+  moveSpeed: 2
-  myLazer: {fileID: 1194927375649470}
+  myLaser: {fileID: 0}
   contribute: 0
 --- !u!136 &136300867333360650
 CapsuleCollider:

unity-environment/Assets/ML-Agents/Examples/Banana/Prefabs/BADBANANA.prefab

   - component: {fileID: 65156783716742152}
   - component: {fileID: 114713238261374398}
   m_Layer: 0
-  m_Name: BAD BANANA
+  m_Name: BadBanana
   m_TagString: badBanana
   m_Icon: {fileID: 0}
   m_NavMeshLayer: 0
   m_MotionVectors: 1
   m_LightProbeUsage: 1
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+  myArea: {fileID: 0}

unity-environment/Assets/ML-Agents/Examples/Banana/Scripts/BananaAgent.cs

     float frozenTime;
     float effectTime;
     Rigidbody agentRB;
-    public float turnSpeed;
-    public float xForce;
-    public float yForce;
-    public float zForce;
+
+    // Speed of agent rotation.
+    public float turnSpeed = 300;
+
+    // Speed of agent movement.
+    public float moveSpeed = 2;
-    public GameObject myLazer;
+    public GameObject myLaser;
+    RayPerception rayPer;
-        agentRB = GetComponent<Rigidbody>(); // cache the RB
+        agentRB = GetComponent<Rigidbody>();
+        rayPer = GetComponent<RayPerception>();
         myAcademy = myAcademyObj.GetComponent<BananaAcademy>();
     }
 
         float[] rayAngles = { 20f, 90f, 160f, 45f, 135f, 70f, 110f };
         string[] detectableObjects = { "banana", "agent", "wall", "badBanana", "frozenAgent" };
-        RayPerception(rayDistance, rayAngles, detectableObjects);
+        AddVectorObs(rayPer.Perceive(rayDistance, rayAngles, detectableObjects, 0f, 0f));
         Vector3 localVelocity = transform.InverseTransformDirection(agentRB.velocity);
         AddVectorObs(localVelocity.x);
         AddVectorObs(localVelocity.z);
 
-
-    public void RayPerception(float rayDistance, float[] rayAngles, string[] detectableObjects) {
-        foreach (float angle in rayAngles)
-        {
-            float noise = 0f;
-            float noisyAngle = angle + Random.Range(-noise, noise);
-            Vector3 position = transform.TransformDirection(GiveCatersian(rayDistance, noisyAngle));
-            Debug.DrawRay(transform.position, position, Color.green, 0.0f, true);
-            RaycastHit hit;
-            float[] subList = new float[detectableObjects.Length + 2];
-            if (Physics.SphereCast(transform.position, 1.0f, position, out hit, rayDistance))
-            {
-                for (int i = 0; i < detectableObjects.Length; i++)
-                {
-                    if (hit.collider.gameObject.CompareTag(detectableObjects[i]))
-                    {
-                        subList[i] = 1;
-                        subList[detectableObjects.Length + 1] = hit.distance / rayDistance;
-                        break;
-                    }
-                }
-            }
-            else
-            {
-                subList[detectableObjects.Length] = 1f;
-            }
-            foreach (float f in subList)
-                AddVectorObs(f);
-        }
-    }
-
-    public Vector3 GiveCatersian(float radius, float angle)
-    {
-        float x = radius * Mathf.Cos(DegreeToRadian(angle));
-        float z = radius * Mathf.Sin(DegreeToRadian(angle));
-        return new Vector3(x, -0.1f, z);
-    }
-
-    public float DegreeToRadian(float degree)
-    {
-        return degree * Mathf.PI / 180f;
-    }
-
     public Color32 ToColor(int HexVal)
     {
         byte R = (byte)((HexVal >> 16) & 0xFF);
                 dirToGo *= 0.5f;
                 agentRB.velocity *= 0.75f;
             }
-            agentRB.AddForce(new Vector3(dirToGo.x * xForce, dirToGo.y * yForce, dirToGo.z * zForce), 
-                             ForceMode.Acceleration);
-            transform.Rotate(rotateDir, Time.deltaTime * turnSpeed);
+            agentRB.AddForce(dirToGo * moveSpeed, ForceMode.VelocityChange);
+            transform.Rotate(rotateDir, Time.fixedDeltaTime * turnSpeed);
         }
 
         if (agentRB.velocity.sqrMagnitude > 25f) // slow it down
 
         if (shoot)
         {
-            myLazer.transform.localScale = new Vector3(1f, 1f, 1f);
-            Vector3 position = transform.TransformDirection(GiveCatersian(25f, 90f));
+            myLaser.transform.localScale = new Vector3(1f, 1f, 1f);
+            Vector3 position = transform.TransformDirection(RayPerception.PolarToCartesian(25f, 90f));
             Debug.DrawRay(transform.position, position, Color.red, 0f, true);
             RaycastHit hit;
             if (Physics.SphereCast(transform.position, 2f, position, out hit, 25f))
         }
         else
         {
-            myLazer.transform.localScale = new Vector3(0f, 0f, 0f);
+            myLaser.transform.localScale = new Vector3(0f, 0f, 0f);
 
         }
 
         shoot = false;
         agentRB.velocity = Vector3.zero;
         bananas = 0;
-        myLazer.transform.localScale = new Vector3(0f, 0f, 0f);
+        myLaser.transform.localScale = new Vector3(0f, 0f, 0f);
         transform.position = new Vector3(Random.Range(-myArea.range, myArea.range), 
                                          2f, Random.Range(-myArea.range, myArea.range)) 
             + area.transform.position;

unity-environment/Assets/ML-Agents/Examples/Crawler/Crawler.unity

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---- !u!114 &241482184
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unity-environment/Assets/ML-Agents/Examples/Hallway/Prefabs/HallwayArea.prefab

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unity-environment/Assets/ML-Agents/Examples/Hallway/Scripts/HallwayAgent.cs

-// Put this script on your blue cube.
-
-using System.Collections;
+using System.Collections;
-    public GameObject ground; // ground game object. we will use the area bounds to spawn the blocks
+    public GameObject ground;
-
-    public GameObject goalA;
-    public GameObject goalB;
-    public GameObject orangeBlock; // the orange block we are going to be pushing
-    public GameObject violetBlock;
-    Rigidbody shortBlockRB;  // cached on initialization
-    Rigidbody agentRB;  // cached on initialization
-    Material groundMaterial; // cached on Awake()
+    public GameObject orangeGoal;
+    public GameObject redGoal;
+    public GameObject orangeBlock;
+    public GameObject redBlock;
+    RayPerception rayPer;
+    Rigidbody shortBlockRB;
+    Rigidbody agentRB;
+    Material groundMaterial;
-
     int selection;
 
     public override void InitializeAgent()
-        brain = FindObjectOfType<Brain>(); // only one brain in the scene so this should find our brain. BRAAAINS.
-
-        agentRB = GetComponent<Rigidbody>(); // cache the agent rigidbody
-        groundRenderer = ground.GetComponent<Renderer>(); // get the ground renderer so we can change the material when a goal is scored
-        groundMaterial = groundRenderer.material; // starting material
-
+        brain = FindObjectOfType<Brain>();
+        rayPer = GetComponent<RayPerception>();
+        agentRB = GetComponent<Rigidbody>();
+        groundRenderer = ground.GetComponent<Renderer>();
+        groundMaterial = groundRenderer.material;
-    public void RayPerception(float rayDistance,
-                                 float[] rayAngles, string[] detectableObjects, float height)
-    {
-        foreach (float angle in rayAngles)
-        {
-            float noise = 0f;
-            float noisyAngle = angle + Random.Range(-noise, noise);
-            Vector3 position = transform.TransformDirection(GiveCatersian(rayDistance, noisyAngle));
-            position.y = height;
-            Debug.DrawRay(transform.position, position, Color.red, 0.1f, true);
-            RaycastHit hit;
-            float[] subList = new float[detectableObjects.Length + 2];
-            if (Physics.SphereCast(transform.position, 1.0f, position, out hit, rayDistance))
-            {
-                for (int i = 0; i < detectableObjects.Length; i++)
-                {
-                    if (hit.collider.gameObject.CompareTag(detectableObjects[i]))
-                    {
-                        subList[i] = 1;
-                        subList[detectableObjects.Length + 1] = hit.distance / rayDistance;
-                        break;
-                    }
-                }
-            }
-            else
-            {
-                subList[detectableObjects.Length] = 1f;
-            }
-            foreach (float f in subList)
-                AddVectorObs(f);
-        }
-    }
-
-    public Vector3 GiveCatersian(float radius, float angle)
-    {
-        float x = radius * Mathf.Cos(DegreeToRadian(angle));
-        float z = radius * Mathf.Sin(DegreeToRadian(angle));
-        return new Vector3(x, 1f, z);
-    }
-
-    public float DegreeToRadian(float degree)
-    {
-        return degree * Mathf.PI / 180f;
-    }
-
-
-        float rayDistance = 8.5f;
-        float[] rayAngles = { 0f, 45f, 90f, 135f, 180f };
-        string[] detectableObjects = { "goal", "orangeBlock", "redBlock", "wall" };
-        RayPerception(rayDistance, rayAngles, detectableObjects, 0f);
+        float rayDistance = 12f;
+        float[] rayAngles = { 20f, 60f, 90f, 120f, 160f };
+        string[] detectableObjects = { "orangeGoal", "redGoal", "orangeBlock", "redBlock", "wall" };
+        AddVectorObs((float)GetStepCount() / (float)agentParameters.maxStep);
+        AddVectorObs(rayPer.Perceive(rayDistance, rayAngles, detectableObjects, 0f, 0f));
-    // swap ground material, wait time seconds, then swap back to the regular ground material.
-        yield return new WaitForSeconds(time); // wait for 2 sec
+        yield return new WaitForSeconds(time);
-
 
     public void MoveAgent(float[] act)
     {
 
-
-        // If we're using Continuous control you will need to change the Action
         if (brain.brainParameters.vectorActionSpaceType == SpaceType.continuous)
         {
             dirToGo = transform.forward * Mathf.Clamp(act[0], -1f, 1f);
         {
             int action = Mathf.FloorToInt(act[0]);
-            if (action == 0)
-            {
-                dirToGo = transform.forward * 1f;
-            }
-            else if (action == 1)
+            switch (action)
-                dirToGo = transform.forward * -1f;
-            }
-            else if (action == 2)
-            {
-                rotateDir = transform.up * 1f;
-            }
-            else if (action == 3)
-            {
-                rotateDir = transform.up * -1f;
+                case 0:
+                    dirToGo = transform.forward * 1f;
+                    break;
+                case 1:
+                    dirToGo = transform.forward * -1f;
+                    break;
+                case 2:
+                    rotateDir = transform.up * 1f;
+                    break;
+                case 3:
+                    rotateDir = transform.up * -1f;
+                    break;
-        transform.Rotate(rotateDir, Time.deltaTime * 100f);
-        agentRB.AddForce(dirToGo * academy.agentRunSpeed, ForceMode.VelocityChange); // GO
+        transform.Rotate(rotateDir, Time.deltaTime * 150f);
+        agentRB.AddForce(dirToGo * academy.agentRunSpeed, ForceMode.VelocityChange);
-        AddReward(-0.0003f);
-
-        MoveAgent(vectorAction); //perform agent actions
-        bool fail = false;  // did the agent or block get pushed off the edge?
-
-        if (!Physics.Raycast(agentRB.position, Vector3.down, 20)) // if the agent has gone over the edge, we done.
-        {
-            fail = true; // fell off bro
-            AddReward(-1f); // BAD AGENT
-                          //transform.position =  GetRandomSpawnPos(agentSpawnAreaBounds, agentSpawnArea);
-            Done(); // if we mark an agent as done it will be reset automatically. AgentReset() will be called.
-        }
-
-        if (fail)
-        {
-            StartCoroutine(GoalScoredSwapGroundMaterial(academy.failMaterial, .5f)); // swap ground material to indicate fail
-        }
+        AddReward(-1f / agentParameters.maxStep);
+        MoveAgent(vectorAction);
-    // detect when we touch the goal
-        if (col.gameObject.CompareTag("goal")) // touched goal
+        if (col.gameObject.CompareTag("orangeGoal") || col.gameObject.CompareTag("redGoal"))
-            if ((selection == 0 && col.gameObject.name == "GoalA") || (selection == 1 && col.gameObject.name == "GoalB"))
+            if ((selection == 0 && col.gameObject.CompareTag("orangeGoal")) ||
+                (selection == 1 && col.gameObject.CompareTag("redGoal")))
-                AddReward(1f); // you get 5 points
-                StartCoroutine(GoalScoredSwapGroundMaterial(academy.goalScoredMaterial, 2)); // swap ground material for a bit to indicate we scored.
+                SetReward(1f);
+                StartCoroutine(GoalScoredSwapGroundMaterial(academy.goalScoredMaterial, 0.5f));
-                AddReward(-0.1f); // you lose a point
-                StartCoroutine(GoalScoredSwapGroundMaterial(academy.failMaterial, .5f)); // swap ground material to indicate fail
+                SetReward(-0.1f);
+                StartCoroutine(GoalScoredSwapGroundMaterial(academy.failMaterial, 0.5f));
-            Done(); // if we mark an agent as done it will be reset automatically. AgentReset() will be called.
+            Done();
-    // In the editor, if "Reset On Done" is checked then AgentReset() will be called automatically anytime we mark done = true in an agent script.
+        float agentOffset = -15f;
+        float blockOffset = 0f;
-            orangeBlock.transform.position = new Vector3(0f + Random.Range(-3f, 3f), 2f, -15f + Random.Range(-5f, 5f)) + ground.transform.position;
-            violetBlock.transform.position = new Vector3(0f, -1000f, -15f + Random.Range(-5f, 5f)) + ground.transform.position;
+            orangeBlock.transform.position =
+                new Vector3(0f + Random.Range(-3f, 3f), 2f, blockOffset + Random.Range(-5f, 5f))
+                + ground.transform.position;
+            redBlock.transform.position =
+                new Vector3(0f, -1000f, blockOffset + Random.Range(-5f, 5f))
+                + ground.transform.position;
-            orangeBlock.transform.position = new Vector3(0f, -1000f, -15f + Random.Range(-5f, 5f)) + ground.transform.position;
-            violetBlock.transform.position = new Vector3(0f, 2f, -15f + Random.Range(-5f, 5f)) + ground.transform.position;
+            orangeBlock.transform.position =
+                           new Vector3(0f, -1000f, blockOffset + Random.Range(-5f, 5f))
+                           + ground.transform.position;
+            redBlock.transform.position =
+                new Vector3(0f, 2f, blockOffset + Random.Range(-5f, 5f))
+                + ground.transform.position;
-        transform.position = new Vector3(0f+ Random.Range(-3f, 3f), 1f, 0f + Random.Range(-5f, 5f)) + ground.transform.position;
+
+        transform.position = new Vector3(0f + Random.Range(-3f, 3f),
+                                         1f, agentOffset + Random.Range(-5f, 5f))
+            + ground.transform.position;
+
+        int goalPos = Random.Range(0, 2);
+        if (goalPos == 0)
+        {
+            orangeGoal.transform.position = new Vector3(7f, 0.5f, 9f) + area.transform.position;
+            redGoal.transform.position = new Vector3(-7f, 0.5f, 9f) + area.transform.position;
+        }
+        else
+        {
+            redGoal.transform.position = new Vector3(7f, 0.5f, 9f) + area.transform.position;
+            orangeGoal.transform.position = new Vector3(-7f, 0.5f, 9f) + area.transform.position;
+        }
-

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unity-environment/Assets/ML-Agents/Examples/PushBlock/Scripts/PushAgentBasic.cs

     Rigidbody blockRB;  //cached on initialization
     Rigidbody agentRB;  //cached on initialization
     Material groundMaterial; //cached on Awake()
+    RayPerception rayPer;
 
     /// <summary>
     /// We will be changing the ground material based on success/failue
         base.InitializeAgent();
         goalDetect = block.GetComponent<GoalDetect>();
         goalDetect.agent = this;
+        rayPer = GetComponent<RayPerception>();
 
         // Cache the agent rigidbody
         agentRB = GetComponent<Rigidbody>();
 
     public override void CollectObservations()
     {
-        // Block position relative to goal.
-        Vector3 blockPosRelToGoal = blockRB.position - goal.transform.position;
-        // Block position relative to agent.
-        Vector3 blockPosRelToAgent = blockRB.position - agentRB.position;
-        // Obstacle position relative to agent.
-
-        // Agent position relative to ground.
-        Vector3 agentPos = agentRB.position - area.transform.position;
-        // Goal position relative to ground.
-        Vector3 goalPos = goal.transform.position - ground.transform.position;
-
-        AddVectorObs(agentPos);
-        AddVectorObs(goalPos);
-        AddVectorObs(blockPosRelToGoal);
-        AddVectorObs(blockPosRelToAgent);
-
-        // Add velocity of block and agent to observations.
-        AddVectorObs(blockRB.velocity);
-        AddVectorObs(agentRB.velocity);
+        float rayDistance = 12f;
+        float[] rayAngles = { 0f, 45f, 90f, 135f, 180f, 110f, 70f };
+        string[] detectableObjects;
+        detectableObjects = new string[] { "block", "goal", "wall" };
+        AddVectorObs(rayPer.Perceive(rayDistance, rayAngles, detectableObjects, 0f, 0f));
+        AddVectorObs(rayPer.Perceive(rayDistance, rayAngles, detectableObjects, 1.5f, 0f));
-    public Vector3 GetRandomSpawnPos(float spawnHeight)
+    public Vector3 GetRandomSpawnPos()
     {
         bool foundNewSpawnLocation = false;
         Vector3 randomSpawnPos = Vector3.zero;
         Done();
 
         // Swap ground material for a bit to indicate we scored.
-        StartCoroutine(GoalScoredSwapGroundMaterial(academy.goalScoredMaterial, 1));
+        StartCoroutine(GoalScoredSwapGroundMaterial(academy.goalScoredMaterial, 0.5f));
     }
 
     /// <summary>
     /// </summary>
 	public void MoveAgent(float[] act)
     {
-        // AGENT ACTIONS
-        // Here we define the actions our agent can use, such as
-        // "go left", "go forward", "turn", etc.
-        // In the brain we  define the number of axes we want to use here. 
-        // In this example we need 2 axes to define:
-        // Right/left movement (act[0])
-        // Forward/back movement (act[1])
+        Vector3 dirToGo = Vector3.zero;
+        Vector3 rotateDir = Vector3.zero;
-        // Example: Right/Left Movement. It is defined in this line:
-        // Vector3 directionX = Vector3.right * Mathf.Clamp(act[0], -1f, 1f);
-
-        // The neural network is setting the act[0] value.
-        // If it chooses 1 then the agent will go right. 
-        // If it chooses -1 the agent will go left. 
-        // If it chooses .42 then it will go a little bit right
-        // If it chooses -.8 then it will go left (well...80% left)
-
-        // Energy Conservation Penalties
-        // Give penalties based on how fast the agent chooses to go. 
-        // The agent should only exert as much energy as necessary.
-        // This is how animals work as well. 
-        // i.e. You're probably not running in place at all times.
-
-        // Larger the value, the less the penalty is.
-        float energyConservPenaltyModifier = 10000;
-
-        // The larger the movement, the greater the penalty given.
-        AddReward(-Mathf.Abs(act[0]) / energyConservPenaltyModifier);
-        AddReward(-Mathf.Abs(act[1]) / energyConservPenaltyModifier);
-
-        Vector3 directionX = Vector3.zero;
-        Vector3 directionZ = Vector3.zero;
-
-
-        // Move left or right in world space.
-        directionX = Vector3.right * Mathf.Clamp(act[0], -1f, 1f);
-
-        // Move forward or back in world space.
-        directionZ = Vector3.forward * Mathf.Clamp(act[1], -1f, 1f);
-
-        // Add directions together. This is the direction we want the agent
-        // to move in.
-        Vector3 dirToGo = directionX + directionZ;
+        int action = Mathf.FloorToInt(act[0]);
-        // Apply movement force!
-        agentRB.AddForce(dirToGo * academy.agentRunSpeed, ForceMode.VelocityChange);
-        if (dirToGo != Vector3.zero)
+        // Goalies and Strikers have slightly different action spaces.
+        switch (action)
-            // Rotate the agent appropriately.
-            agentRB.rotation = Quaternion.Lerp(agentRB.rotation,
-                                               Quaternion.LookRotation(dirToGo),
-                                               Time.deltaTime * academy.agentRotationSpeed);
+            case 0:
+                dirToGo = transform.forward * 1f;
+                break;
+            case 1:
+                dirToGo = transform.forward * -1f;
+                break;
+            case 2:
+                rotateDir = transform.up * 1f;
+                break;
+            case 3:
+                rotateDir = transform.up * -1f;
+                break;
+            case 4:
+                dirToGo = transform.right * -0.75f;
+                break;
+            case 5:
+                dirToGo = transform.right * 0.75f;
+                break;
+        transform.Rotate(rotateDir, Time.fixedDeltaTime * 200f);
+        agentRB.AddForce(dirToGo * academy.agentRunSpeed,
+                         ForceMode.VelocityChange);
 
     }
 
         MoveAgent(vectorAction);
 
         // Penalty given each step to encourage agent to finish task quickly.
-        AddReward(-.00005f);
-
-        // Did the agent or block get pushed off the edge?
-        bool fail = false;
-
-        // If the agent has gone over the edge, end the episode.
-        if (!Physics.Raycast(agentRB.position, Vector3.down, 3))
-        {
-            // Fell off bro
-            fail = true;
-
-            // BAD AGENT
-            SetReward(-1f);
-
-            // If we mark an agent as done it will be reset automatically. 
-            // AgentReset() will be called.
-            Done();
-        }
-
-        // If the block has gone over the edge, end the episode.
-        if (!Physics.Raycast(blockRB.position, Vector3.down, 3))
-        {
-            // Fell off bro
-            fail = true;
-
-            // BAD AGENT
-            SetReward(-1f);
-
-            // If we mark an agent as done it will be reset automatically. 
-            // AgentReset() will be called.
-            Done();
-        }
-
-        if (fail)
-        {
-            // Swap ground material to indicate failure of the episode.
-            StartCoroutine(GoalScoredSwapGroundMaterial(academy.failMaterial, 1f));
-        }
+        AddReward(-1f / agentParameters.maxStep);
     }
 
     /// <summary>
     {
         // Get a random position for the block.
-        block.transform.position = GetRandomSpawnPos(1.5f);
+        block.transform.position = GetRandomSpawnPos();
 
         // Reset block velocity back to zero.
         blockRB.velocity = Vector3.zero;
         area.transform.Rotate(new Vector3(0f, rotationAngle, 0f));
 
         ResetBlock();
-        transform.position = GetRandomSpawnPos(1.5f);
+        transform.position = GetRandomSpawnPos();
-

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unity-environment/Assets/ML-Agents/Examples/Soccer/Prefabs/SoccerBall/Prefabs/SoccerBall.prefab

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   agentTag: agent
   redGoalTag: redGoal
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unity-environment/Assets/ML-Agents/Examples/Soccer/Scenes/SoccerTwos.unity

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unity-environment/Assets/ML-Agents/Examples/Soccer/Scripts/AgentSoccer.cs

     [HideInInspector]
     public Rigidbody agentRB;
     SoccerAcademy academy;
-    Renderer renderer;
+    Renderer agentRenderer;
+    RayPerception rayPer;
-        renderer.material = team == Team.red ? academy.redMaterial : academy.blueMaterial;
+        agentRenderer.material = team == Team.red ? academy.redMaterial : academy.blueMaterial;
     }
 
     public void JoinRedTeam(AgentRole role)
-        renderer.material = academy.redMaterial;
+        agentRenderer.material = academy.redMaterial;
     }
 
     public void JoinBlueTeam(AgentRole role)
-        renderer.material = academy.blueMaterial;
+        agentRenderer.material = academy.blueMaterial;
-    void Awake()
+    public override void InitializeAgent()
-        renderer = GetComponent<Renderer>();
+        base.InitializeAgent();
+        agentRenderer = GetComponent<Renderer>();
+        rayPer = GetComponent<RayPerception>();
         academy = FindObjectOfType<SoccerAcademy>();
         PlayerState playerState = new PlayerState();
         playerState.agentRB = GetComponent<Rigidbody>();
         playerState.playerIndex = playerIndex;
     }
 
-    public override void InitializeAgent()
-    {
-        base.InitializeAgent();
-    }
-
-    public void RayPerception(float rayDistance,
-                             float[] rayAngles, string[] detectableObjects,
-                              float startHeight, float endHeight)
-    {
-        foreach (float angle in rayAngles)
-        {
-            float noise = 0f;
-            float noisyAngle = angle + Random.Range(-noise, noise);
-            Vector3 position = transform.TransformDirection(
-                GiveCatersian(rayDistance, noisyAngle));
-            position.y = endHeight;
-            Debug.DrawRay(transform.position + new Vector3(0f, startHeight, 0f),
-                          position, Color.red, 0.1f, true);
-            RaycastHit hit;
-            float[] subList = new float[detectableObjects.Length + 2];
-            if (Physics.SphereCast(transform.position +
-                                   new Vector3(0f, startHeight, 0f), 1.0f,
-                                   position, out hit, rayDistance))
-            {
-                for (int i = 0; i < detectableObjects.Length; i++)
-                {
-                    if (hit.collider.gameObject.CompareTag(detectableObjects[i]))
-                    {
-                        subList[i] = 1;
-                        subList[detectableObjects.Length + 1] = hit.distance / rayDistance;
-                        break;
-                    }
-                }
-            }
-            else
-            {
-                subList[detectableObjects.Length] = 1f;
-            }
-            foreach (float f in subList)
-                AddVectorObs(f);
-        }
-    }
-
-    public Vector3 GiveCatersian(float radius, float angle)
-    {
-        float x = radius * Mathf.Cos(DegreeToRadian(angle));
-        float z = radius * Mathf.Sin(DegreeToRadian(angle));
-        return new Vector3(x, 1f, z);
-    }
-
-    public float DegreeToRadian(float degree)
-    {
-        return degree * Mathf.PI / 180f;
-    }
-
     public override void CollectObservations()
     {
         float rayDistance = 20f;
             detectableObjects = new string[] { "ball", "blueGoal", "redGoal",
                 "wall", "blueAgent", "redAgent" };
         }
-        RayPerception(rayDistance, rayAngles, detectableObjects, 0f, 0f);
-        RayPerception(rayDistance, rayAngles, detectableObjects, 1f, 1f);
+        AddVectorObs(rayPer.Perceive(rayDistance, rayAngles, detectableObjects, 0f, 0f));
+        AddVectorObs(rayPer.Perceive(rayDistance, rayAngles, detectableObjects, 1f, 0f));
     }
 
     public void MoveAgent(float[] act)
         if (agentRole == AgentRole.goalie)
         {
             kickPower = 0f;
-            if (action == 0)
+            switch (action)
-                dirToGo = transform.forward * 1f;
-                kickPower = 1f;
+                case 0:
+                    dirToGo = transform.forward * 1f;
+                    kickPower = 1f;
+                    break;
+                case 1:
+                    dirToGo = transform.forward * -1f;
+                    break;
+                case 3:
+                    dirToGo = transform.right * -1f;
+                    break;
+                case 2:
+                    dirToGo = transform.right * 1f;
+                    break;
-            else if (action == 1)
-            {
-                dirToGo = transform.forward * -1f;
-            }
-            else if (action == 3)
-            {
-                dirToGo = transform.right * -1f;
-            }
-            else if (action == 2)
-            {
-                dirToGo = transform.right * 1f;
-            }
-
-            if (action == 0)
+            switch (action)
-                dirToGo = transform.forward * 1f;
-                kickPower = 1f;
-            }
-            else if (action == 1)
-            {
-                dirToGo = transform.forward * -1f;
-            }
-            else if (action == 2)
-            {
-                rotateDir = transform.up * 1f;
-            }
-            else if (action == 3)
-            {
-                rotateDir = transform.up * -1f;
-            }
-            else if (action == 4)
-            {
-                dirToGo = transform.right * -1f;
-            }
-            else if (action == 5)
-            {
-                dirToGo = transform.right * 1f;
+                case 0:
+                    dirToGo = transform.forward * 1f;
+                    kickPower = 1f;
+                    break;
+                case 1:
+                    dirToGo = transform.forward * -1f;
+                    break;
+                case 2:
+                    rotateDir = transform.up * 1f;
+                    break;
+                case 3:
+                    rotateDir = transform.up * -1f;
+                    break;
+                case 4:
+                    dirToGo = transform.right * -0.75f;
+                    break;
+                case 5:
+                    dirToGo = transform.right * 0.75f;
+                    break;
             }
         }
         transform.Rotate(rotateDir, Time.deltaTime * 100f);

unity-environment/Assets/ML-Agents/Examples/SharedAssets/Materials/SuccessGround.mat

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unity-environment/Assets/ML-Agents/Examples/SharedAssets/Materials/MaterialsGrid/goalMaterial.mat

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unity-environment/Assets/ML-Agents/Examples/SharedAssets/Materials/MaterialsGrid/agentMaterial.mat

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unity-environment/Assets/ML-Agents/Examples/SharedAssets/Materials/MaterialsBall/Materials/logo2.mat

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unity-environment/Assets/ML-Agents/Examples/SharedAssets/Materials/MaterialsBall/Materials/logo1.mat

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unity-environment/Assets/ML-Agents/Scripts/Brain.cs

      * the state
      * <br> If discrete : The number of possible values the state can take*/
 
-    [Range(1, 10)]
+    [Range(1, 50)]
     public int numStackedVectorObservations = 1;
 
     public int vectorActionSize = 1;

unity-environment/Assets/ML-Agents/Scripts/ExternalCommunicator.cs

     StreamWriter logWriter;
     string logPath;
 
-    const string api = "API-2";
+    const string _version_ = "API-3";
 
     /// Placeholder for state information to send.
     [System.Serializable]
         accParamerters.brainParameters = new List<BrainParameters>();
         accParamerters.brainNames = new List<string>();
         accParamerters.externalBrainNames = new List<string>();
-        accParamerters.apiNumber = api;
+        accParamerters.apiNumber = _version_;
         accParamerters.logPath = logPath;
         foreach (Brain b in brains)
         {

unity-environment/ProjectSettings/TagManager.asset

   - blueGoal
   - blueAgent
   - redAgent
+  - redBlock
+  - goal
+  - ground
+  - orangeBlock
+  - block
+  - orangeGoal
   layers:
   - Default
   - TransparentFX

docs/Feature-On-Demand-Decisions.md

+# On Demand Decision Making
+
+## Description
+On demand decision making allows agents to request decisions from their 
+brains only when needed instead of receiving decisions at a fixed 
+frequency. This is useful when the agents commit to an action for a 
+variable number of steps or when the agents cannot make decisions 
+at the same time. This typically the case for turn based games, games 
+where agents must react to events or games where agents can take 
+actions of variable duration.
+
+## How to use
+
+To enable or disable on demand decision making, use the checkbox called
+`On Demand Decisions` in the Agent Inspector.
+
+<p align="center">
+    <img src="images/ml-agents-ODD.png" 
+        alt="On Demand Decision" 
+        width="500" border="10" />
+</p>
+
+ * If `On Demand Decisions` is not checked, the Agent will request a new 
+ decision every `Decision Frequency` steps and 
+ perform an action every step. In the example above, 
+ `CollectObservations()` will be called every 5 steps and 
+ `AgentAction()` will be called at every step. This means that the 
+ Agent will reuse the decision the Brain has given it. 
+
+ * If `On Demand Decisions` is checked, the Agent controls when to receive
+ decisions, and take actions. To do so, the Agent may leverage one or two methods:
+   * `RequestDecision()` Signals that the Agent is requesting a decision.
+   This causes the Agent to collect its observations and ask the Brain for a 
+   decision at the next step of the simulation. Note that when an Agent 
+   requests a decision, it also request an action. 
+   This is to ensure that all decisions lead to an action during training.
+   * `RequestAction()` Signals that the Agent is requesting an action. The
+   action provided to the Agent in this case is the same action that was
+   provided the last time it requested a decision. 

docs/Migrating-v0.3.md

+# Migrating to ML-Agents v0.3
+
+There are a large number of new features and improvements in ML-Agents v0.3 which change both the training process and Unity API in ways which will cause incompatibilities with environments made using older versions. This page is designed to highlight those changes for users familiar with v0.1 or v0.2 in order to ensure a smooth transition.
+
+## Important
+ * ML-Agents is no longer compatible with Python 2. 
+
+## Python Training
+ * The training script `ppo.py` and `PPO.ipynb` Python notebook have been replaced with a single `learn.py` script as the launching point for training with ML-Agents. For more information on using `learn.py`, see [here]().
+ * Hyperparameters for training brains are now stored in the `trainer_config.yaml` file. For more information on using this file, see [here]().
+
+## Unity API
+ * Modifications to an Agent's rewards must now be done using either `AddReward()` or `SetReward()`.
+ * Setting an Agent to done now requires the use of the `Done()` method.
+ * `CollectStates()` has been replaced by `CollectObservations()`, which now no longer returns a list of floats.
+ * To collect observations, call `AddVectorObs()` within `CollectObservations()`. Note that you can call `AddVectorObs()` with floats, integers, lists and arrays of floats, Vector3 and Quaternions. 
+ * `AgentStep()` has been replaced by `AgentAction()`.
+ * `WaitTime()` has been removed.
+ * The `Frame Skip` field of the Academy is replaced by the Agent's `Decision Frequency` field, enabling agent to make decisions at different frequencies.
+
+## Semantics
+In order to more closely align with the terminology used in the Reinforcement Learning field, and to be more descriptive, we have changed the names of some of the concepts used in ML-Agents. The changes are highlighted in the table below.
+
+| Old - v0.2 and earlier | New - v0.3 and later |
+| --- | --- |
+| State | Vector Observation |
+| Observation | Visual Observation |
+| Action | Vector Action |
+| N/A | Text Observation |
+| N/A | Text Action |

unity-environment/Assets/ML-Agents/Examples/Banana/Prefabs/RLArea.prefab.meta

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unity-environment/Assets/ML-Agents/Examples/SharedAssets/Scripts/RayPerception.cs

+using System.Collections.Generic;
+using UnityEngine;
+
+/// <summary>
+/// Ray perception component. Attach this to agents to enable "local perception"
+/// via the use of ray casts directed outward from the agent. 
+/// </summary>
+public class RayPerception : MonoBehaviour
+{
+    List<float> perceptionBuffer = new List<float>();
+    Vector3 endPosition;
+    RaycastHit hit;
+    /// <summary>
+    /// Creates perception vector to be used as part of an observation of an agent.
+    /// </summary>
+    /// <returns>The partial vector observation corresponding to the set of rays</returns>
+    /// <param name="rayDistance">Radius of rays</param>
+    /// <param name="rayAngles">Anlges of rays (starting from (1,0) on unit circle).</param>
+    /// <param name="detectableObjects">List of tags which correspond to object types agent can see</param>
+    /// <param name="startOffset">Starting heigh offset of ray from center of agent.</param>
+    /// <param name="endOffset">Ending height offset of ray from center of agent.</param>
+    public List<float> Perceive(float rayDistance,
+                         float[] rayAngles, string[] detectableObjects,
+                          float startOffset, float endOffset)
+    {
+        perceptionBuffer.Clear();
+        // For each ray sublist stores categorial information on detected object
+        // along with object distance.
+        foreach (float angle in rayAngles)
+        {
+            endPosition = transform.TransformDirection(
+                PolarToCartesian(rayDistance, angle));
+            endPosition.y = endOffset;
+            if (Application.isEditor)
+            {
+                Debug.DrawRay(transform.position + new Vector3(0f, startOffset, 0f),
+              endPosition, Color.black, 0.01f, true);
+            }
+            float[] subList = new float[detectableObjects.Length + 2];
+            if (Physics.SphereCast(transform.position +
+                                   new Vector3(0f, startOffset, 0f), 0.5f,
+                                   endPosition, out hit, rayDistance))
+            {
+                for (int i = 0; i < detectableObjects.Length; i++)
+                {
+                    if (hit.collider.gameObject.CompareTag(detectableObjects[i]))
+                    {
+                        subList[i] = 1;
+                        subList[detectableObjects.Length + 1] = hit.distance / rayDistance;
+                        break;
+                    }
+                }
+            }
+            else
+            {
+                subList[detectableObjects.Length] = 1f;
+            }
+            perceptionBuffer.AddRange(subList);
+        }
+        return perceptionBuffer;
+    }
+
+    /// <summary>
+    /// Converts polar coordinate to cartesian coordinate.
+    /// </summary>
+    public static Vector3 PolarToCartesian(float radius, float angle)
+    {
+        float x = radius * Mathf.Cos(DegreeToRadian(angle));
+        float z = radius * Mathf.Sin(DegreeToRadian(angle));
+        return new Vector3(x, 0f, z);
+    }
+
+    /// <summary>
+    /// Converts degrees to radians.
+    /// </summary>
+    public static float DegreeToRadian(float degree)
+    {
+        return degree * Mathf.PI / 180f;
+    }
+}

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unity-environment/Assets/ML-Agents/Examples/WallJump/Scripts/WallJumpAcademy.cs

+using System.Collections;
+using System.Collections.Generic;
+using UnityEngine;
+
+public class WallJumpAcademy : Academy {
+
+    [Header("Specific to WallJump")]
+	public float agentRunSpeed;
+    public float agentJumpHeight;
+    //when a goal is scored the ground will use this material for a few seconds.
+	public Material goalScoredMaterial; 
+    //when fail, the ground will use this material for a few seconds. 
+    public Material failMaterial;
+
+    [HideInInspector]
+    //use ~3 to make things less floaty
+	public float gravityMultiplier = 2.5f; 
+    [HideInInspector]
+	public float agentJumpVelocity = 777;
+    [HideInInspector]
+	public float agentJumpVelocityMaxChange = 10;
+
+    // Use this for initialization
+    public override void InitializeAcademy()
+    {
+        Physics.gravity *= gravityMultiplier;
+    }
+}

unity-environment/Assets/ML-Agents/Examples/WallJump/Scripts/WallJumpAcademy.cs.meta

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unity-environment/Assets/ML-Agents/Examples/WallJump/Scripts/WallJumpAgent.cs

+//Put this script on your blue cube.
+
+using System.Collections;
+using System.Collections.Generic;
+using UnityEngine;
+using System.Linq;
+
+public class WallJumpAgent : Agent
+{
+    // Depending on this value, the wall will have different height
+    int configuration;
+    // Brain to use when no wall is present
+    public Brain noWallBrain;
+    // Brain to use when a jumpable wall is present
+    public Brain smallWallBrain;
+    // Brain to use when a wall requiring a block to jump over is present
+    public Brain bigWallBrain;
+
+    public GameObject ground;
+    public GameObject spawnArea;
+    Bounds spawnAreaBounds;
+
+
+    public GameObject goal;
+    public GameObject shortBlock;
+    public GameObject wall;
+    Rigidbody shortBlockRB;
+    Rigidbody agentRB;
+    Material groundMaterial;
+    Renderer groundRenderer;
+    WallJumpAcademy academy;
+    RayPerception rayPer;
+
+    public float jumpingTime;
+    public float jumpTime;
+    // This is a downward force applied when falling to make jumps look
+    // less floaty
+    public float fallingForce;
+    // Use to check the coliding objects
+    public Collider[] hitGroundColliders = new Collider[3];
+    Vector3 jumpTargetPos;
+    Vector3 jumpStartingPos;
+
+    string[] detectableObjects;
+
+    public override void InitializeAgent()
+    {
+        academy = FindObjectOfType<WallJumpAcademy>();
+        rayPer = GetComponent<RayPerception>();
+        configuration = Random.Range(0, 5);
+        detectableObjects = new string[] { "wall", "goal", "block" };
+
+        agentRB = GetComponent<Rigidbody>();
+        shortBlockRB = shortBlock.GetComponent<Rigidbody>();
+        spawnAreaBounds = spawnArea.GetComponent<Collider>().bounds;
+        groundRenderer = ground.GetComponent<Renderer>();
+        groundMaterial = groundRenderer.material;
+
+        spawnArea.SetActive(false);
+    }
+
+
+    // Begin the jump sequence
+    public void Jump()
+    {
+
+        jumpingTime = 0.2f;
+        jumpStartingPos = agentRB.position;
+    }
+
+    /// <summary>
+    /// Does the ground check.
+    /// </summary>
+    /// <returns><c>true</c>, if the agent is on the ground, 
+    /// <c>false</c> otherwise.</returns>
+    /// <param name="boxWidth">The width of the box used to perform 
+    /// the ground check. </param>
+    public bool DoGroundCheck(float boxWidth)
+    {
+        hitGroundColliders = new Collider[3];
+        Physics.OverlapBoxNonAlloc(
+            gameObject.transform.position + new Vector3(0, -0.05f, 0),
+                            new Vector3(boxWidth / 2f, 0.5f, boxWidth / 2f),
+                            hitGroundColliders,
+                            gameObject.transform.rotation);
+        bool grounded = false;
+        foreach (Collider col in hitGroundColliders)
+        {
+
+            if (col != null && col.transform != this.transform &&
+                (col.CompareTag("walkableSurface") ||
+                col.CompareTag("block") ||
+                 col.CompareTag("wall")))
+            {
+                grounded = true; //then we're grounded
+                break;
+            }
+        }
+        return grounded;
+    }
+
+
+    /// <summary>
+    /// Moves  a rigidbody towards a position smoothly.
+    /// </summary>
+    /// <param name="targetPos">Target position.</param>
+    /// <param name="rb">The rigidbody to be moved.</param>
+    /// <param name="targetVel">The velocity to target during the
+    ///  motion.</param>
+    /// <param name="maxVel">The maximum velocity posible.</param>
+    void MoveTowards(
+        Vector3 targetPos, Rigidbody rb, float targetVel, float maxVel)
+    {
+        Vector3 moveToPos = targetPos - rb.worldCenterOfMass;
+        Vector3 velocityTarget = moveToPos * targetVel * Time.fixedDeltaTime;
+        if (float.IsNaN(velocityTarget.x) == false)
+        {
+            rb.velocity = Vector3.MoveTowards(
+                rb.velocity, velocityTarget, maxVel);
+        }
+    }
+
+    public override void CollectObservations()
+    {
+        float rayDistance = 20f;
+        float[] rayAngles = { 0f, 45f, 90f, 135f, 180f, 110f, 70f };
+        AddVectorObs(rayPer.Perceive(
+            rayDistance, rayAngles, detectableObjects, 0f, 0f));
+        AddVectorObs(rayPer.Perceive(
+            rayDistance, rayAngles, detectableObjects, 2.5f, 2.5f));
+        Vector3 agentPos = agentRB.position - ground.transform.position;
+
+        AddVectorObs(agentPos / 20f);
+        AddVectorObs(DoGroundCheck(0.4f) ? 1 : 0);
+
+
+    }
+
+    /// <summary>
+    /// Gets a random spawn position in the spawningArea.
+    /// </summary>
+    /// <returns>The random spawn position.</returns>
+    public Vector3 GetRandomSpawnPos()
+    {
+        Vector3 randomSpawnPos = Vector3.zero;
+        float randomPosX = Random.Range(-spawnAreaBounds.extents.x,
+                                        spawnAreaBounds.extents.x);
+        float randomPosZ = Random.Range(-spawnAreaBounds.extents.z,
+                                        spawnAreaBounds.extents.z);
+
+        randomSpawnPos = spawnArea.transform.position +
+                                  new Vector3(randomPosX, 0.45f, randomPosZ);
+        return randomSpawnPos;
+    }
+
+    /// <summary>
+    /// Chenges the color of the ground for a moment
+    /// </summary>
+    /// <returns>The Enumerator to be used in a Coroutine</returns>
+    /// <param name="mat">The material to be swaped.</param>
+    /// <param name="time">The time the material will remain.</param>
+    IEnumerator GoalScoredSwapGroundMaterial(Material mat, float time)
+    {
+        groundRenderer.material = mat;
+        yield return new WaitForSeconds(time); //wait for 2 sec
+        groundRenderer.material = groundMaterial;
+    }
+
+
+    public void MoveAgent(float[] act)
+    {
+
+        AddReward(-0.0005f);
+        bool smallGrounded = DoGroundCheck(0.4f);
+        bool largeGrounded = DoGroundCheck(1.0f);
+
+        Vector3 dirToGo = Vector3.zero;
+        Vector3 rotateDir = Vector3.zero;
+
+        int action = Mathf.FloorToInt(act[0]);
+        switch (action)
+        {
+            case 0:
+                dirToGo = transform.forward * 1f * (largeGrounded ? 1f : 0.5f);
+                break;
+            case 1:
+                dirToGo = transform.forward * -1f * (largeGrounded ? 1f : 0.5f);
+                break;
+            case 2:
+
+                rotateDir = transform.up * -1f;
+                break;
+            case 3:
+                rotateDir = transform.up * 1f;
+                break;
+            case 4:
+                dirToGo = transform.right * -0.6f * (largeGrounded ? 1f : 0.5f);
+                break;
+            case 5:
+                dirToGo = transform.right * 0.6f * (largeGrounded ? 1f : 0.5f);
+                break;
+            case 6:
+                if ((jumpingTime <= 0f) && smallGrounded)
+                {
+                    Jump();
+                }
+                break;
+        }
+
+        transform.Rotate(rotateDir, Time.fixedDeltaTime * 300f);
+        agentRB.AddForce(dirToGo * academy.agentRunSpeed,
+                         ForceMode.VelocityChange);
+
+        if (jumpingTime > 0f)
+        {
+            jumpTargetPos =
+            new Vector3(agentRB.position.x,
+                        jumpStartingPos.y + academy.agentJumpHeight,
+                        agentRB.position.z) + dirToGo;
+            MoveTowards(jumpTargetPos, agentRB, academy.agentJumpVelocity,
+                        academy.agentJumpVelocityMaxChange);
+
+        }
+
+        if (!(jumpingTime > 0f) && !largeGrounded)
+        {
+            agentRB.AddForce(
+            Vector3.down * fallingForce, ForceMode.Acceleration);
+        }
+        jumpingTime -= Time.fixedDeltaTime;
+    }
+
+    public override void AgentAction(float[] vectorAction, string textAction)
+    {
+        MoveAgent(vectorAction);
+        if ((!Physics.Raycast(agentRB.position, Vector3.down, 20))
+            || (!Physics.Raycast(shortBlockRB.position, Vector3.down, 20)))
+        {
+            Done();
+            SetReward(-1f);
+            ResetBlock(shortBlockRB);
+            StartCoroutine(
+                GoalScoredSwapGroundMaterial(academy.failMaterial, .5f));
+        }
+    }
+
+    // Detect when the agent hits the goal
+    void OnTriggerEnter(Collider col)
+    {
+        if (col.gameObject.CompareTag("goal") && DoGroundCheck(0.4f))
+        {
+            SetReward(1f);
+            Done();
+            StartCoroutine(
+                GoalScoredSwapGroundMaterial(academy.goalScoredMaterial, 2));
+        }
+    }
+
+
+    //Reset the orange block position
+    void ResetBlock(Rigidbody blockRB)
+    {
+        blockRB.transform.position = GetRandomSpawnPos();
+        blockRB.velocity = Vector3.zero;
+        blockRB.angularVelocity = Vector3.zero;
+    }
+
+    public override void AgentReset()
+    {
+        ResetBlock(shortBlockRB);
+        transform.localPosition = new Vector3(
+            18 * (Random.value - 0.5f), 1, -12);
+        configuration = Random.Range(0, 5);
+        agentRB.velocity = default(Vector3);
+
+    }
+
+    private void FixedUpdate()
+    {
+        if (configuration != -1)
+        {
+            ConfigureAgent(configuration);
+            configuration = -1;
+        }
+    }
+
+    /// <summary>
+    /// Configures the agent. Given an integer config, the wall will have
+    /// different height and a different brain will be assigned to the agent.
+    /// </summary>
+    /// <param name="config">Config. 
+    /// If 0 : No wall and noWallBrain.
+    /// If 1:  Small wall and smallWallBrain.
+    /// Other : Tall wall and BigWallBrain. </param>
+    void ConfigureAgent(int config)
+    {
+        if (config == 0)
+        {
+            wall.transform.localScale = new Vector3(
+                wall.transform.localScale.x,
+                academy.resetParameters["no_wall_height"],
+                wall.transform.localScale.z);
+            GiveBrain(noWallBrain);
+        }
+        else if (config == 1)
+        {
+            wall.transform.localScale = new Vector3(
+                wall.transform.localScale.x,
+                academy.resetParameters["small_wall_height"],
+                wall.transform.localScale.z);
+            GiveBrain(smallWallBrain);
+        }
+        else
+        {
+            float height =
+                academy.resetParameters["big_wall_min_height"] +
+                Random.value * (academy.resetParameters["big_wall_max_height"] -
+                academy.resetParameters["big_wall_min_height"]);
+            wall.transform.localScale = new Vector3(
+                wall.transform.localScale.x,
+                height,
+                wall.transform.localScale.z);
+            GiveBrain(bigWallBrain);
+        }
+    }
+}
+

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docs/Feature-On-Demand-Decision.md

-# On Demand Decision Making
-
-## Description
-On demand decision making allows agents to request decisions from their 
-brains only when needed instead of requesting decisions at a fixed 
-frequency. This is useful when the agents commit to an action for a 
-variable number of steps or when the agents cannot make decisions 
-at the same time. This typically the case for turn based games, games 
-where agents must react to events or games where agents can take 
-actions of variable duration.
-
-## How to use
-
-In the agent inspector, there is a checkbox called 
-`On Demand Decision`
-
-![Brain Inspector](images/ml-agents-ODD.png)
-
- * If `On Demand Decision` is not checked, all the agents will 
- request a new decision every `Decision Frequency` steps and 
- perform an action every step. In the example above, 
- `CollectObservations()` will be called every 5 steps and 
- `AgentAct()` will be called at every step. This means that the 
- agent will reuse the decision the brain has given it. 
-
- * If `On Demand Decision` is checked, you are in charge of telling 
- the agent when to request a decision and when to request an action. 
- To do so, call the following methods on your agent component.
-   * `RequestDecision()` Call this method to signal the agent that it 
-   must collect its observations and ask the brain for a decision at 
-   the next step of the simulation. Note that when an agent requests 
-   a decision, it will also request an action automatically 
-   (This is to ensure that all decisions lead to an action during training)
-   * `RequestAction()` Call this method to signal the agent that 
-   it must reuse its previous action at the next step of the 
-   simulation. The Agent will not ask the brain for a new decision, 
-   it will just call `AgentAct()` with the same action.

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