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

# Conflicts:
#	unity-environment/Assets/ML-Agents/Editor/MLAgentsEditModeTest.cs
#	unity-environment/Assets/ML-Agents/Examples/Basic/Scripts/BasicAgent.cs
#	unity-environment/Assets/ML-Agents/Scripts/Academy.cs

docs/ML-Agents-Overview.md

 The [Imitation Learning](Training-Imitation-Learning.md) tutorial covers this training
 mode with the **Anti-Graviator** sample environment.
 
+### Recurrent Neural Networks
+In some scenarios, agents must learn to remember the past in order to take the 
+best decision. When an agent only has partial observability of the environment, 
+keeping track of past observations can help the agent learn. We provide an 
+implementation of [LSTM](https://en.wikipedia.org/wiki/Long_short-term_memory) in 
+our trainers that enable the agent to store memories to be used in future steps.
+
+The [Training with LSTM](Training-LSTM.md) tutorial covers this feature and 
+the **Hallway** environment demonstrates its capabilities.
+
 ## Flexible Training Scenarios
 
 While the discussion so-far has mostly focused on training a single agent, with 
 However, this could also be helpful for the Heuristic and Internal Brains,
 particularly when debugging agent behaviors. You can learn more about using 
 the broadcasting feature [here](Feature-Broadcasting.md).
+
+* **On Demand Decision** - With ML-Agents it is possible to have agents 
+request decisions only when needed as opposed to requesting decisions at 
+every step. This enables training of turn based games, games where agents 
+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](Learning-Environment-On-Demand-Decision)

docs/Readme.md

  * [Training with Proximal Policy Optimization](Training-PPO.md)
  * [Training with Curriculum Learning](Training-Curriculum-Learning.md)
  * [Training with Imitation Learning](Training-Imitation-Learning.md)
+ * [Training with LSTM](Training-LSTM.md)
  * [Training on the Cloud with Amazon Web Services](Training-on-Amazon-Web-Service.md)
  * [Using TensorBoard to Observe Training](Using-Tensorboard.md)
 

python/unitytrainers/ppo/trainer.py

         values = self.sess.run(run_list, feed_dict=feed_dict)
         run_out = dict(zip(run_list, values))
         self.stats['value_estimate'].append(run_out[self.model.value].mean())
-        self.stats['entropy'].append(run_out[self.model.entropy])
+        self.stats['entropy'].append(run_out[self.model.entropy].mean())
         self.stats['learning_rate'].append(run_out[self.model.learning_rate])
         if self.use_recurrent:
             return (run_out[self.model.output],

unity-environment/Assets/ML-Agents/Editor/AgentEditor.cs

         EditorGUILayout.PropertyField(brain);
 
         EditorGUILayout.LabelField("Agent Cameras");
-		for (int i = 0; i < cameras.arraySize; i++)
-		{
-			EditorGUILayout.PropertyField(cameras.GetArrayElementAtIndex(i), new GUIContent("Camera " + (i + 1).ToString() + ": "));
-		}
+        for (int i = 0; i < cameras.arraySize; i++)
+        {
+            EditorGUILayout.PropertyField(cameras.GetArrayElementAtIndex(i), new GUIContent("Camera " + (i + 1).ToString() + ": "));
+        }
-		if (GUILayout.Button("Remove Camera", EditorStyles.miniButton))
-		{
-			cameras.arraySize--;
-		}
+        if (GUILayout.Button("Remove Camera", EditorStyles.miniButton))
+        {
+            cameras.arraySize--;
+        }
-		
+        
-                                		"The per-agent maximum number of steps."));
+                                        "The per-agent maximum number of steps."));
         EditorGUILayout.PropertyField(isResetOnDone, new GUIContent("Reset On Done",
                          "If checked, the agent will reset on done. Else, AgentOnDone() will be called."));
         EditorGUILayout.PropertyField(isEBS, new GUIContent("On Demand Decision", "If checked, you must manually request decisions."));
             actionsPerDecision.intValue = Mathf.Max(1, actionsPerDecision.intValue);
         }
 
-		serializedAgent.ApplyModifiedProperties();
+        serializedAgent.ApplyModifiedProperties();
 
         EditorGUILayout.LabelField("", GUI.skin.horizontalSlider);
         base.OnInspectorGUI();

unity-environment/Assets/ML-Agents/Editor/BrainEditor.cs

     [SerializeField]
     bool _Foldout = true;
 
-	public override void OnInspectorGUI ()
-	{
-		Brain myBrain = (Brain)target;
-		SerializedObject serializedBrain = serializedObject;
+    public override void OnInspectorGUI ()
+    {
+        Brain myBrain = (Brain)target;
+        SerializedObject serializedBrain = serializedObject;
-		if (myBrain.transform.parent == null) {
-			EditorGUILayout.HelpBox ("A Brain GameObject must be a child of an Academy GameObject!", MessageType.Error);
-		} else if (myBrain.transform.parent.GetComponent<Academy> () == null) {
-			EditorGUILayout.HelpBox ("The Parent of a Brain must have an Academy Component attached to it!", MessageType.Error);
-		}
+        if (myBrain.transform.parent == null) {
+            EditorGUILayout.HelpBox ("A Brain GameObject must be a child of an Academy GameObject!", MessageType.Error);
+        } else if (myBrain.transform.parent.GetComponent<Academy> () == null) {
+            EditorGUILayout.HelpBox ("The Parent of a Brain must have an Academy Component attached to it!", MessageType.Error);
+        }
-		BrainParameters parameters = myBrain.brainParameters;
+        BrainParameters parameters = myBrain.brainParameters;
-		
-		serializedBrain.Update();
-		
+        
+        serializedBrain.Update();
+        
 
         _Foldout = EditorGUILayout.Foldout(_Foldout, "Brain Parameters");
         int indentLevel = EditorGUI.indentLevel;
 
         }
         EditorGUI.indentLevel = indentLevel;
-		SerializedProperty bt = serializedBrain.FindProperty("brainType");
-		EditorGUILayout.PropertyField(bt);
+        SerializedProperty bt = serializedBrain.FindProperty("brainType");
+        EditorGUILayout.PropertyField(bt);
-		if (bt.enumValueIndex < 0) {
-			bt.enumValueIndex = (int)BrainType.Player;
-		}
+        if (bt.enumValueIndex < 0) {
+            bt.enumValueIndex = (int)BrainType.Player;
+        }
-		serializedBrain.ApplyModifiedProperties();
+        serializedBrain.ApplyModifiedProperties();
-		myBrain.UpdateCoreBrains ();
-		myBrain.coreBrain.OnInspector ();
+        myBrain.UpdateCoreBrains ();
+        myBrain.coreBrain.OnInspector ();
-		#if !NET_4_6 && ENABLE_TENSORFLOW
-		EditorGUILayout.HelpBox ("You cannot have ENABLE_TENSORFLOW without NET_4_6", MessageType.Error);
-		#endif
-	}
+        #if !NET_4_6 && ENABLE_TENSORFLOW
+        EditorGUILayout.HelpBox ("You cannot have ENABLE_TENSORFLOW without NET_4_6", MessageType.Error);
+        #endif
+    }
 }

unity-environment/Assets/ML-Agents/Editor/MLAgentsEditModeTest.cs

-using UnityEngine;
+using UnityEngine;
 using NUnit.Framework;
 using System.Reflection;
 

unity-environment/Assets/ML-Agents/Examples/3DBall/Scripts/Ball3DDecision.cs

 
 public class Ball3DDecision : MonoBehaviour, Decision
 {
+    public float rotationSpeed = 2f;
+
-            List<float> ret = new List<float>();
-            if (state[2] < 0 || state[5] < 0)
-            {
-                ret.Add(state[5]);
-            }
-            else
-            {
-                ret.Add(state[5]);
-            }
-            if (state[3] < 0 || state[7] < 0)
-            {
-                ret.Add(-state[7]);
-            }
-            else
-            {
-                ret.Add(-state[7]);
-            }
-            return ret.ToArray();
+            List<float> act = new List<float>();
+
+            // state[5] is the velocity of the ball in the x orientation. 
+            // We use this number to control the Platform's z axis rotation speed, 
+            // so that the Platform is tilted in the x orientation correspondingly. 
+            act.Add(state[5] * rotationSpeed);
+
+            // state[7] is the velocity of the ball in the z orientation. 
+            // We use this number to control the Platform's x axis rotation speed,  
+            // so that the Platform is tilted in the z orientation correspondingly. 
+            act.Add(-state[7] * rotationSpeed);
+            return act.ToArray();
+
+        // If the vector action space type is discrete, then we don't do anything.     
         else
         {
             return new float[1]{ 1f };

unity-environment/Assets/ML-Agents/Examples/Area/Scripts/Area.cs

 
 public class Area : MonoBehaviour {
 
-	// Use this for initialization
-	void Start () {
-		
-	}
-	
-	// Update is called once per frame
-	void Update () {
-		
-	}
+    // Use this for initialization
+    void Start () {
+        
+    }
+    
+    // Update is called once per frame
+    void Update () {
+        
+    }
 
     public virtual void ResetArea() {
         

unity-environment/Assets/ML-Agents/Examples/Area/Scripts/AreaAgent.cs

     public override void CollectObservations()
     {
         Vector3 velocity = GetComponent<Rigidbody>().velocity;
-		AddVectorObs((transform.position.x - area.transform.position.x));
-		AddVectorObs((transform.position.y - area.transform.position.y));
-		AddVectorObs((transform.position.z + 5 - area.transform.position.z));
-		AddVectorObs(velocity.x);
-		AddVectorObs(velocity.y);
-		AddVectorObs(velocity.z);
-	}
+        AddVectorObs((transform.position.x - area.transform.position.x));
+        AddVectorObs((transform.position.y - area.transform.position.y));
+        AddVectorObs((transform.position.z + 5 - area.transform.position.z));
+        AddVectorObs(velocity.x);
+        AddVectorObs(velocity.y);
+        AddVectorObs(velocity.z);
+    }
 
     public void MoveAgent(float[] act) {
         float directionX = 0;
         AddReward(-0.005f);
         MoveAgent(vectorAction);
 
-		if (gameObject.transform.position.y < 0.0f || Mathf.Abs(gameObject.transform.position.x - area.transform.position.x) > 8f || 
+        if (gameObject.transform.position.y < 0.0f || Mathf.Abs(gameObject.transform.position.x - area.transform.position.x) > 8f || 
-		{
+        {
-		}
-	}
+        }
+    }
-	public override void AgentReset()
-	{
-		transform.position = new Vector3(Random.Range(-3.5f, 3.5f), 1.1f, -8f) + area.transform.position;
-		GetComponent<Rigidbody>().velocity = new Vector3(0f, 0f, 0f);
+    public override void AgentReset()
+    {
+        transform.position = new Vector3(Random.Range(-3.5f, 3.5f), 1.1f, -8f) + area.transform.position;
+        GetComponent<Rigidbody>().velocity = new Vector3(0f, 0f, 0f);
-		area.GetComponent<Area>().ResetArea();
-	}
+        area.GetComponent<Area>().ResetArea();
+    }
 
 }

unity-environment/Assets/ML-Agents/Examples/Area/Scripts/AreaDecision.cs

 public class AreaDecision : MonoBehaviour, Decision {
 
     public float[] Decide (List<float> state, List<Texture2D> observation, float reward, bool done, List<float> memory)
-	{
+    {
-	}
+    }
-	public List<float> MakeMemory (List<float> state, List<Texture2D> observation, float reward, bool done, List<float> memory)
-	{
-		return new List<float>();
-		
-	}
+    public List<float> MakeMemory (List<float> state, List<Texture2D> observation, float reward, bool done, List<float> memory)
+    {
+        return new List<float>();
+        
+    }
 }

unity-environment/Assets/ML-Agents/Examples/Area/Scripts/GoalInteract.cs

             agent.Done();
             agent.AddReward(1f);
         }
-	}
+    }
 
 }

unity-environment/Assets/ML-Agents/Examples/Area/Scripts/Push/PushAcademy.cs

     public float blockSize;
     public float xVariation;
 
-	public override void AcademyReset()
-	{
+    public override void AcademyReset()
+    {
-	}
+    }
-	public override void AcademyStep()
-	{
+    public override void AcademyStep()
+    {
-	}
+    }
 
 }

unity-environment/Assets/ML-Agents/Examples/Area/Scripts/Push/PushAgent.cs

 
 public class PushAgent : AreaAgent
 {
-	public GameObject goalHolder;
+    public GameObject goalHolder;
     public GameObject block;
 
     Rigidbody rb;
 
     public override void InitializeAgent()
     {
-		base.InitializeAgent();
+        base.InitializeAgent();
-	public override void CollectObservations()
-	{
+    public override void CollectObservations()
+    {
         velocity = rb.velocity;
         blockVelocity = block.GetComponent<Rigidbody>().velocity;
         AddVectorObs((transform.position.x - area.transform.position.x));
         AddVectorObs((block.transform.position.y - area.transform.position.y));
         AddVectorObs((block.transform.position.z + 5 - area.transform.position.z));
 
-		AddVectorObs(velocity.x);
-		AddVectorObs(velocity.y);
-		AddVectorObs(velocity.z);
+        AddVectorObs(velocity.x);
+        AddVectorObs(velocity.y);
+        AddVectorObs(velocity.z);
-		AddVectorObs(blockVelocity.x);
-		AddVectorObs(blockVelocity.y);
-		AddVectorObs(blockVelocity.z);
+        AddVectorObs(blockVelocity.x);
+        AddVectorObs(blockVelocity.y);
+        AddVectorObs(blockVelocity.z);
-	}
+    }
 
     public override void AgentAction(float[] vectorAction, string textAction)
 	{
         if (gameObject.transform.position.y < 0.0f || Mathf.Abs(gameObject.transform.position.x - area.transform.position.x) > 8f ||
             Mathf.Abs(gameObject.transform.position.z + 5 - area.transform.position.z) > 8)
-		{
+        {
-		}
-	}
+        }
+    }
-	public override void AgentReset()
-	{
+    public override void AgentReset()
+    {
-		rb.velocity = new Vector3(0f, 0f, 0f);
+        rb.velocity = new Vector3(0f, 0f, 0f);
-	}
+    }
-	public override void AgentOnDone()
-	{
+    public override void AgentOnDone()
+    {
-	}
+    }
 }

unity-environment/Assets/ML-Agents/Examples/Area/Scripts/Push/PushArea.cs

     public GameObject goalHolder;
     public GameObject academy;
 
-	// Use this for initialization
-	void Start () {
-		academy = GameObject.Find("Academy");
-	}
-	
-	// Update is called once per frame
-	void Update () {
-		
-	}
+    // Use this for initialization
+    void Start () {
+        academy = GameObject.Find("Academy");
+    }
+    
+    // Update is called once per frame
+    void Update () {
+        
+    }
-	public override void ResetArea()
-	{
+    public override void ResetArea()
+    {
         float goalSize = academy.GetComponent<PushAcademy>().goalSize;
         float blockSize = academy.GetComponent<PushAcademy>().blockSize;
         float xVariation = academy.GetComponent<PushAcademy>().xVariation;
         blockSize = Random.Range(blockSize * 0.9f, blockSize * 1.1f);
         block.transform.localScale = new Vector3(blockSize, 1f, blockSize);
         goalHolder.transform.localScale = new Vector3(goalSize, 1f, goalSize);
-	}
+    }
 
 }

unity-environment/Assets/ML-Agents/Examples/Area/Scripts/Wall/WallAcademy.cs

     public float minWallHeight;
     public float maxWallHeight;
 
-	public override void AcademyReset()
-	{
+    public override void AcademyReset()
+    {
-	}
+    }
-	public override void AcademyStep()
-	{
+    public override void AcademyStep()
+    {
 
     }
 

unity-environment/Assets/ML-Agents/Examples/Area/Scripts/Wall/WallAgent.cs

 
 public class WallAgent : AreaAgent
 {
-	public GameObject goalHolder;
+    public GameObject goalHolder;
-		base.InitializeAgent();
+        base.InitializeAgent();
-	public override void CollectObservations()
-	{
+    public override void CollectObservations()
+    {
         Vector3 velocity = GetComponent<Rigidbody>().velocity;
         AddVectorObs((transform.position.x - area.transform.position.x));
         AddVectorObs((transform.position.y - area.transform.position.y));
         AddVectorObs((block.transform.position.y - area.transform.position.y));
         AddVectorObs((block.transform.position.z + 5 - area.transform.position.z));
 
-		AddVectorObs(wall.transform.localScale.y);
+        AddVectorObs(wall.transform.localScale.y);
-		AddVectorObs(velocity.x);
-		AddVectorObs(velocity.y);
-		AddVectorObs(velocity.z);
+        AddVectorObs(velocity.x);
+        AddVectorObs(velocity.y);
+        AddVectorObs(velocity.z);
-		AddVectorObs(blockVelocity.x);
-		AddVectorObs(blockVelocity.y);
-		AddVectorObs(blockVelocity.z);
-	}
+        AddVectorObs(blockVelocity.x);
+        AddVectorObs(blockVelocity.y);
+        AddVectorObs(blockVelocity.z);
+    }
 
     public override void AgentAction(float[] vectorAction, string textAction)
 	{
         if (gameObject.transform.position.y < 0.0f ||
             Mathf.Abs(gameObject.transform.position.x - area.transform.position.x) > 8f ||
             Mathf.Abs(gameObject.transform.position.z + 5 - area.transform.position.z) > 8)
-		{
+        {
-		}
-	}
+        }
+    }
-	public override void AgentReset()
-	{
-		transform.position = new Vector3(Random.Range(-3.5f, 3.5f), 1.1f, -8f) + area.transform.position;
-		GetComponent<Rigidbody>().velocity = new Vector3(0f, 0f, 0f);
+    public override void AgentReset()
+    {
+        transform.position = new Vector3(Random.Range(-3.5f, 3.5f), 1.1f, -8f) + area.transform.position;
+        GetComponent<Rigidbody>().velocity = new Vector3(0f, 0f, 0f);
-	}
+    }
-	public override void AgentOnDone()
-	{
+    public override void AgentOnDone()
+    {
-	}
+    }
 }

unity-environment/Assets/ML-Agents/Examples/Area/Scripts/Wall/WallArea.cs

     public GameObject block;
     public GameObject goalHolder;
 
-	// Use this for initialization
-	void Start () {
-		academy = GameObject.Find("Academy");
-	}
-	
-	// Update is called once per frame
-	void Update () {
-		
-	}
+    // Use this for initialization
+    void Start () {
+        academy = GameObject.Find("Academy");
+    }
+    
+    // Update is called once per frame
+    void Update () {
+        
+    }
-		wall.transform.localScale = new Vector3(12f, Random.Range(wallHeightMin, wallHeightMax) - 0.1f, 1f);
+        wall.transform.localScale = new Vector3(12f, Random.Range(wallHeightMin, wallHeightMax) - 0.1f, 1f);
-		goalHolder.transform.position = new Vector3(Random.Range(-3.5f, 3.5f), -0.1f, 0f) + gameObject.transform.position;
-	}
+        goalHolder.transform.position = new Vector3(Random.Range(-3.5f, 3.5f), -0.1f, 0f) + gameObject.transform.position;
+    }
 }

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

     public override void InitializeAgent()
     {
         base.InitializeAgent();
-        agentRB = GetComponent<Rigidbody>(); //cache the RB
+        agentRB = GetComponent<Rigidbody>(); // cache the RB
         Monitor.verticalOffset = 1f;
         myArea = area.GetComponent<BananaArea>();
         myAcademy = myAcademyObj.GetComponent<BananaAcademy>();
             transform.Rotate(rotateDir, Time.deltaTime * turnSpeed);
         }
 
-        if (agentRB.velocity.sqrMagnitude > 25f) //slow it down
+        if (agentRB.velocity.sqrMagnitude > 25f) // slow it down
         {
             agentRB.velocity *= 0.95f;
         }

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

     public bool respawn;
     public BananaArea myArea;
 
-	// Use this for initialization
-	void Start () {
-		
-	}
-	
-	// Update is called once per frame
-	void Update () {
-		
-	}
+    // Use this for initialization
+    void Start () {
+        
+    }
+    
+    // Update is called once per frame
+    void Update () {
+        
+    }
 
     public void OnEaten() {
         if (respawn) 

unity-environment/Assets/ML-Agents/Examples/Basic/Scripts/BasicAcademy.cs

 
 public class BasicAcademy : Academy {
 
-	public override void AcademyReset()
-	{
+    public override void AcademyReset()
+    {
-	}
+    }
-	public override void AcademyStep()
-	{
+    public override void AcademyStep()
+    {
-	}
+    }
 
 }

unity-environment/Assets/ML-Agents/Examples/Bouncer/Scripts/BouncerAcademy.cs

 
 public class BouncerAcademy : Academy {
 
-	public override void AcademyReset()
-	{
+    public override void AcademyReset()
+    {
-	}
+    }
-	public override void AcademyStep()
-	{
+    public override void AcademyStep()
+    {
-	}
+    }
 
 }

unity-environment/Assets/ML-Agents/Examples/Bouncer/Scripts/BouncerAgent.cs

         rb.velocity = rb.velocity.normalized * speed;
     }
 
-	public override void CollectObservations()
-	{
+    public override void CollectObservations()
+    {
-	}
+    }
 
     public override void AgentAction(float[] vectorAction, string textAction)
 	{
         }
         else
         {
-            //AddReward(0.05f);
+            // AddReward(0.05f);
-	}
+    }
-	public override void AgentReset()
-	{
+    public override void AgentReset()
+    {
-	}
+    }
-	public override void AgentOnDone()
-	{
+    public override void AgentOnDone()
+    {
-	}
+    }
 
     private void OnTriggerEnter(Collider collision)
     {

unity-environment/Assets/ML-Agents/Examples/Bouncer/Scripts/BouncerBanana.cs

     void Start(){
 
     }
-	
-	// Update is called once per frame
-	void FixedUpdate () {
+    
+    // Update is called once per frame
+    void FixedUpdate () {
-	}
+    }
 
     private void OnTriggerEnter(Collider collision)
     {

unity-environment/Assets/ML-Agents/Examples/Crawler/Scripts/CameraFollow.cs

 
 public class CameraFollow : MonoBehaviour {
 
-	public Transform target;
-	Vector3 offset;
+    public Transform target;
+    Vector3 offset;
-	// Use this for initialization
-	void Start () {
-		offset = gameObject.transform.position - target.position;
-	}
-	
-	// Update is called once per frame
-	void Update () {
-        //gameObject.transform.position = target.position + offset;
+    // Use this for initialization
+    void Start () {
+        offset = gameObject.transform.position - target.position;
+    }
+    
+    // Update is called once per frame
+    void Update () {
+        // gameObject.transform.position = target.position + offset;
-	}
+    }
 }

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

   resetParameters:
     resetParameters:
     - key: gridSize
-      value: 3
+      value: 5
     - key: numObstacles
       value: 1
     - key: numGoals
   m_Father: {fileID: 0}
   m_RootOrder: 1
   m_LocalEulerAnglesHint: {x: 90, y: 0, z: 0}
---- !u!114 &215565037
+--- !u!114 &181601419
 MonoBehaviour:
   m_ObjectHideFlags: 0
   m_PrefabParentObject: {fileID: 0}
   m_EditorHideFlags: 0
   m_Script: {fileID: 11500000, guid: 41e9bda8f3cf1492fa74926a530f6f70, type: 3}
-  m_Name: (Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)
+  m_Name: (Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)(Clone)
   m_EditorClassIdentifier: 
   broadcast: 1
   continuousPlayerActions: []
   - key: 275
     value: 3
   defaultAction: -1
-  brain: {fileID: 1535917239}
---- !u!114 &219137297
-MonoBehaviour:
-  m_ObjectHideFlags: 0
-  m_PrefabParentObject: {fileID: 0}
-  m_PrefabInternal: {fileID: 0}
-  m_GameObject: {fileID: 0}
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unity-environment/Assets/ML-Agents/Examples/GridWorld/TFModels/GridWorld_3x3.bytes.meta

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

 
 public class HallwayAcademy : Academy {
 
-	public float agentRunSpeed; 
-	public float agentRotationSpeed;
-    public Material goalScoredMaterial; //when a goal is scored the ground will use this material for a few seconds.
-    public Material failMaterial; //when fail, the ground will use this material for a few seconds. 
-	public float gravityMultiplier; //use ~3 to make things less floaty
+    public float agentRunSpeed; 
+    public float agentRotationSpeed;
+    public Material goalScoredMaterial; // when a goal is scored the ground will use this material for a few seconds.
+    public Material failMaterial; // when fail, the ground will use this material for a few seconds. 
+    public float gravityMultiplier; // use ~3 to make things less floaty
 
     public override void InitializeAcademy()
     {
-	public override void AcademyReset()
-	{
+    public override void AcademyReset()
+    {
 
     }
 }

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

-//Put this script on your blue cube.
+// Put this script on your blue cube.
 
 using System.Collections;
 using System.Collections.Generic;
 {
-    public GameObject ground; //ground game object. we will use the area bounds to spawn the blocks
+    public GameObject ground; // ground game object. we will use the area bounds to spawn the blocks
-    public GameObject orangeBlock; //the orange block we are going to be pushing
+    public GameObject orangeBlock; // the orange block we are going to be pushing
-    Rigidbody shortBlockRB;  //cached on initialization
-    Rigidbody agentRB;  //cached on initialization
-    Material groundMaterial; //cached on Awake()
+    Rigidbody shortBlockRB;  // cached on initialization
+    Rigidbody agentRB;  // cached on initialization
+    Material groundMaterial; // cached on Awake()
     Renderer groundRenderer;
     HallwayAcademy academy;
 
     {
         base.InitializeAgent();
         academy = FindObjectOfType<HallwayAcademy>();
-        brain = FindObjectOfType<Brain>(); //only one brain in the scene so this should find our brain. BRAAAINS.
+        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
+        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
 
     }
 
         RayPerception(rayDistance, rayAngles, detectableObjects, 0f);
     }
 
-    //swap ground material, wait time seconds, then swap back to the regular ground material.
+    // 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); // wait for 2 sec
         groundRenderer.material = groundMaterial;
     }
 
         Vector3 dirToGo = Vector3.zero;
         Vector3 rotateDir = Vector3.zero;
 
-        //If we're using Continuous control you will need to change the Action
+
+        // 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);
             }
         }
         transform.Rotate(rotateDir, Time.deltaTime * 100f);
-        agentRB.AddForce(dirToGo * academy.agentRunSpeed, ForceMode.VelocityChange); //GO
+        agentRB.AddForce(dirToGo * academy.agentRunSpeed, ForceMode.VelocityChange); // GO
     }
 
     public override void AgentAction(float[] vectorAction, string textAction)
         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.
+        if (!Physics.Raycast(agentRB.position, Vector3.down, 20)) // if the agent has gone over the edge, we done.
-            fail = true; //fell off bro
+            fail = true; // fell off bro
-            Done(); //if we mark an agent as done it will be reset automatically. AgentReset() will be called.
+            Done(); // if we mark an agent as done it will be reset automatically. AgentReset() will be called.
-            StartCoroutine(GoalScoredSwapGroundMaterial(academy.failMaterial, .5f)); //swap ground material to indicate fail
+            StartCoroutine(GoalScoredSwapGroundMaterial(academy.failMaterial, .5f)); // swap ground material to indicate fail
         }
     }
 
-        if (col.gameObject.CompareTag("goal")) //touched goal
+        if (col.gameObject.CompareTag("goal")) // touched goal
-                AddReward(1f); //you get 5 points
-                StartCoroutine(GoalScoredSwapGroundMaterial(academy.goalScoredMaterial, 2)); //swap ground material for a bit to indicate we scored.
+                AddReward(1f); // you get 5 points
+                StartCoroutine(GoalScoredSwapGroundMaterial(academy.goalScoredMaterial, 2)); // swap ground material for a bit to indicate we scored.
-                AddReward(-0.1f); //you lose a point
-                StartCoroutine(GoalScoredSwapGroundMaterial(academy.failMaterial, .5f)); //swap ground material to indicate fail
+                AddReward(-0.1f); // you lose a point
+                StartCoroutine(GoalScoredSwapGroundMaterial(academy.failMaterial, .5f)); // swap ground material to indicate fail
-            Done(); //if we mark an agent as done it will be reset automatically. AgentReset() will be called.
+            Done(); // if we mark an agent as done it will be reset automatically. AgentReset() will be called.
-    //In the editor, if "Reset On Done" is checked then AgentReset() will be called automatically anytime we mark done = true in an agent script.
+    // In the editor, if "Reset On Done" is checked then AgentReset() will be called automatically anytime we mark done = true in an agent script.
     public override void AgentReset()
     {
         selection = Random.Range(0, 2);

unity-environment/Assets/ML-Agents/Examples/Reacher/Scene.unity

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unity-environment/Assets/ML-Agents/Examples/Reacher/Scripts/FlyCamera.cs

     space : Moves camera on X and Z axis only.  So camera doesn't gain any height*/
 
 
-    public float mainSpeed = 100.0f; //regular speed
-    public float shiftAdd = 250.0f; //multiplied by how long shift is held.  Basically running
-    public float maxShift = 1000.0f; //Maximum speed when holdin gshift
-    public float camSens = 0.25f; //How sensitive it with mouse
+    public float mainSpeed = 100.0f; // regular speed
+    public float shiftAdd = 250.0f; // multiplied by how long shift is held.  Basically running
+    public float maxShift = 1000.0f; // Maximum speed when holdin gshift
+    public float camSens = 0.25f; // How sensitive it with mouse
-    private Vector3 lastMouse = new Vector3(255, 255, 255); //kind of in the middle of the screen, rather than at the top (play)
+    private Vector3 lastMouse = new Vector3(255, 255, 255); // kind of in the middle of the screen, rather than at the top (play)
     private float totalRun = 1.0f;
 
     void Awake()
-                                                                    //transform.position.Set(0,8,-32);
-                                                                    //transform.rotation.Set(15,0,0,1);
+                                                                    // transform.position.Set(0,8,-32);
+                                                                    // transform.rotation.Set(15,0,0,1);
         transform.position = new Vector3(0, 8, -32);
         transform.rotation = Quaternion.Euler(25, 0, 0);
     }
             lastMouse = new Vector3(transform.eulerAngles.x + lastMouse.x, transform.eulerAngles.y + lastMouse.y, 0);
             transform.eulerAngles = lastMouse;
             lastMouse = Input.mousePosition;
-            //Mouse  camera angle done.  
+            // Mouse  camera angle done.  
-        //Keyboard commands
+        // Keyboard commands
         Vector3 p = GetBaseInput();
         if (Input.GetKey(KeyCode.LeftShift))
         {
         Vector3 newPosition = transform.position;
         if (Input.GetKey(KeyCode.Space)
             || (movementStaysFlat && !(rotateOnlyIfMousedown && Input.GetMouseButton(1))))
-        { //If player wants to move on X and Z axis only
+        { // If player wants to move on X and Z axis only
             transform.Translate(p);
             newPosition.x = transform.position.x;
             newPosition.z = transform.position.z;
     }
 
     private Vector3 GetBaseInput()
-    { //returns the basic values, if it's 0 than it's not active.
+    { // returns the basic values, if it's 0 than it's not active.
         Vector3 p_Velocity = new Vector3();
         if (Input.GetKey(KeyCode.W))
         {

unity-environment/Assets/ML-Agents/Examples/Reacher/Scripts/ReacherAcademy.cs

     public float goalSpeed;
 
 
-	public override void AcademyReset()
-	{
+    public override void AcademyReset()
+    {
-	}
+    }
-	public override void AcademyStep()
-	{
+    public override void AcademyStep()
+    {
-	}
+    }
 
 }

unity-environment/Assets/ML-Agents/Examples/Reacher/Scripts/ReacherAgent.cs

     /// We collect the normalized rotations, angularal velocities, and velocities of both
     /// limbs of the reacher as well as the relative position of the target and hand.
     /// </summary>
-	public override void CollectObservations()
-	{
+    public override void CollectObservations()
+    {
         AddVectorObs(pendulumA.transform.rotation.eulerAngles.x / 180.0f - 1.0f);
         AddVectorObs(pendulumA.transform.rotation.eulerAngles.y / 180.0f - 1.0f);
         AddVectorObs(pendulumA.transform.rotation.eulerAngles.z / 180.0f - 1.0f);
 
         goal.transform.localScale = new Vector3(goalSize, goalSize, goalSize);
     }
-
 }

unity-environment/Assets/ML-Agents/Examples/Reacher/Scripts/ReacherDecision.cs

 public class ReacherDecision : MonoBehaviour, Decision {
 
     public float[] Decide (List<float> state, List<Texture2D> observation, float reward, bool done, List<float> memory)
-	{
+    {
         float[] action = new float[4];
         for (int i = 0; i < 4; i++) {
             action[i] = Random.Range(-1f, 1f);
-	}
+    }
-	{
-		return new List<float>();
-		
-	}
+    {
+        return new List<float>();
+        
+    }
 }

unity-environment/Assets/ML-Agents/Examples/Reacher/Scripts/ReacherGoal.cs

     public GameObject hand;
     public GameObject goalOn;
 
-	// Use this for initialization
-	void Start () {
-		
-	}
-	
-	// Update is called once per frame
-	void Update () {
-		
-	}
+    // Use this for initialization
+    void Start () {
+        
+    }
+    
+    // Update is called once per frame
+    void Update () {
+        
+    }
 
     private void OnTriggerEnter(Collider other)
     {

unity-environment/Assets/ML-Agents/Examples/Tennis/Prefabs/TennisArea.prefab

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unity-environment/Assets/ML-Agents/Examples/Tennis/Scripts/TennisArea.cs

     public GameObject agentA;
     public GameObject agentB;
 
-	// Use this for initialization
-	void Start () {
+    // Use this for initialization
+    void Start () {
-	}
-	
-	// Update is called once per frame
-	void Update () {
-		
-	}
+    }
+    
+    // Update is called once per frame
+    void Update () {
+        
+    }
 
     public void MatchReset() {
         float ballOut = Random.Range(6f, 8f);

unity-environment/Assets/ML-Agents/Examples/Tennis/Scripts/hitWall.cs

 
 public class hitWall : MonoBehaviour
 {
-	public GameObject areaObject;
-	public int lastAgentHit;
+    public GameObject areaObject;
+    public int lastAgentHit;
-	// Use this for initialization
-	void Start()
-	{
-		lastAgentHit = -1;
-	}
+    // Use this for initialization
+    void Start()
+    {
+        lastAgentHit = -1;
+    }
-	private void OnTriggerExit(Collider other)
-	{
-		TennisArea area = areaObject.GetComponent<TennisArea>();
-		TennisAgent agentA = area.agentA.GetComponent<TennisAgent>();
-		TennisAgent agentB = area.agentB.GetComponent<TennisAgent>();
+    private void OnTriggerExit(Collider other)
+    {
+        TennisArea area = areaObject.GetComponent<TennisArea>();
+        TennisAgent agentA = area.agentA.GetComponent<TennisAgent>();
+        TennisAgent agentB = area.agentB.GetComponent<TennisAgent>();
-		if (other.name == "over")
-		{
-			if (lastAgentHit == 0)
-			{
+        if (other.name == "over")
+        {
+            if (lastAgentHit == 0)
+            {
-			}
-			else
-			{
+            }
+            else
+            {
-			}
-			lastAgentHit = 0;
+            }
+            lastAgentHit = 0;
-		}
-	}
+        }
+    }
-	private void OnCollisionEnter(Collision collision)
-	{
-		TennisArea area = areaObject.GetComponent<TennisArea>();
-		TennisAgent agentA = area.agentA.GetComponent<TennisAgent>();
-		TennisAgent agentB = area.agentB.GetComponent<TennisAgent>();
+    private void OnCollisionEnter(Collision collision)
+    {
+        TennisArea area = areaObject.GetComponent<TennisArea>();
+        TennisAgent agentA = area.agentA.GetComponent<TennisAgent>();
+        TennisAgent agentB = area.agentB.GetComponent<TennisAgent>();
-		if (collision.gameObject.tag == "iWall")
-		{
-			if (collision.gameObject.name == "wallA")
-			{
-				if (lastAgentHit == 0)
-				{
+        if (collision.gameObject.tag == "iWall")
+        {
+            if (collision.gameObject.name == "wallA")
+            {
+                if (lastAgentHit == 0)
+                {
-					agentB.score += 1;
-				}
-				else
-				{
-					agentA.SetReward(0);
+                    agentB.score += 1;
+                }
+                else
+                {
+                    agentA.SetReward(0);
-					agentA.score += 1;
-				}
-			}
-			else if (collision.gameObject.name == "wallB")
-			{
-				if (lastAgentHit == 0)
-				{
+                    agentA.score += 1;
+                }
+            }
+            else if (collision.gameObject.name == "wallB")
+            {
+                if (lastAgentHit == 0)
+                {
-					agentB.score += 1;
-				}
-				else
-				{
-					agentA.SetReward(0);
+                    agentB.score += 1;
+                }
+                else
+                {
+                    agentA.SetReward(0);
-					agentA.score += 1;
-				}
-			}
-			else if (collision.gameObject.name == "floorA")
-			{
-				if (lastAgentHit == 0 || lastAgentHit == -1)
-				{
+                    agentA.score += 1;
+                }
+            }
+            else if (collision.gameObject.name == "floorA")
+            {
+                if (lastAgentHit == 0 || lastAgentHit == -1)
+                {
-					agentB.SetReward(0);
-					agentB.score += 1;
-				}
-				else
-				{
+                    agentB.SetReward(0);
+                    agentB.score += 1;
+                }
+                else
+                {
-					agentB.SetReward(0);
-					agentB.score += 1;
+                    agentB.SetReward(0);
+                    agentB.score += 1;
-				}
-			}
-			else if (collision.gameObject.name == "floorB")
-			{
-				if (lastAgentHit == 1 || lastAgentHit == -1)
-				{
-					agentA.SetReward(0);
+                }
+            }
+            else if (collision.gameObject.name == "floorB")
+            {
+                if (lastAgentHit == 1 || lastAgentHit == -1)
+                {
+                    agentA.SetReward(0);
-					agentA.score += 1;
-				}
-				else
-				{
-					agentA.SetReward(0);
+                    agentA.score += 1;
+                }
+                else
+                {
+                    agentA.SetReward(0);
-					agentA.score += 1;
-				}
-			}
-			else if (collision.gameObject.name == "net")
-			{
-				if (lastAgentHit == 0)
-				{
+                    agentA.score += 1;
+                }
+            }
+            else if (collision.gameObject.name == "net")
+            {
+                if (lastAgentHit == 0)
+                {
-					agentB.SetReward(0);
-					agentB.score += 1;
-				}
-				else
-				{
-					agentA.SetReward(0);
+                    agentB.SetReward(0);
+                    agentB.score += 1;
+                }
+                else
+                {
+                    agentA.SetReward(0);
-					agentA.score += 1;
-				}
-			}
+                    agentA.score += 1;
+                }
+            }
-			agentB.Done();
-			area.MatchReset();
-		}
+            agentB.Done();
+            area.MatchReset();
+        }
-		if (collision.gameObject.tag == "agent")
-		{
-			if (collision.gameObject.name == "AgentA")
-			{
-				lastAgentHit = 0;
-			}
-			else
-			{
-				lastAgentHit = 1;
-			}
-		}
-	}
+        if (collision.gameObject.tag == "agent")
+        {
+            if (collision.gameObject.name == "AgentA")
+            {
+                lastAgentHit = 0;
+            }
+            else
+            {
+                lastAgentHit = 1;
+            }
+        }
+    }
 }

unity-environment/Assets/ML-Agents/Examples/Tennis/TFModels/Tennis.bytes.meta

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

-using System.Collections.Generic;
+using System.Collections.Generic;
 using UnityEngine;
 
 /**
         }
         return brains;
     }
-}
+}

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

     /// <summary>
     /// The brain that will control this agent. 
     /// Use the inspector to drag the desired brain gameObject into
-	/// the Brain field.
+    /// the Brain field.
     ///</summary>
     [HideInInspector]
     public Brain brain;
     ///</summary>
     private float reward;
 
-    /// Whether or not the agent is requests an action
+    /// Whether or not the agent requests an action.
-    /// Whether or not the agent is requests a decision
+    /// Whether or not the agent requests a decision.
     private bool requestDecision;
 
     /// <summary> 
     private int id;
 
     /// <summary>
-    /// Unity method called when the agent is istanciated or set to active.
+    /// Unity method called when the agent is instantiated or set to active.
     /// </summary>
     private void OnEnable()
     {
     /// When GiveBrain is called, the agent unsubscribes from its 
     /// previous brain and subscribes to the one passed in argument.
     /// Use this method to provide a brain to the agent via script. 
-	///<param name= "b" >The Brain the agent will subscribe to.</param>
+    ///<param name= "b" >The Brain the agent will subscribe to.</param>
     /// <summary>
     public void GiveBrain(Brain b)
     {
     /// <summary>
     /// Is called when the agent must request the brain for a new decision.
     /// </summary>
-	public void RequestDecision()
+    public void RequestDecision()
     {
         requestDecision = true;
         RequestAction();
     /// </summary>
-	public void RequestAction()
+    public void RequestAction()
     {
         requestAction = true;
     }
     /// <summary>
     /// Initialize the agent with this method
     /// Must be implemented in agent-specific child class.
-	/// This method called only once when the agent is enabled.
+    /// This method called only once when the agent is enabled.
     /// </summary>
     public virtual void InitializeAgent()
     {
                 {
                     if (!hasAlreadyReset)
                     {
-                        //If event based, the agent can reset as soon
+                        // If event based, the agent can reset as soon
                         // as it is done
                         _AgentReset();
                         hasAlreadyReset = true;

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

     public KeyCode recordKey = KeyCode.R;
     public KeyCode resetKey = KeyCode.C;
 
-	// Use this for initialization
-	void Start () {
+    // Use this for initialization
+    void Start () {
-	}
-	
-	// Update is called once per frame
-	void Update () {
+    }
+    
+    // Update is called once per frame
+    void Update () {
         if (Input.GetKeyDown(recordKey))
         {
             recordExperiences = !recordExperiences;
         Monitor.Log("Recording experiences " + recordKey.ToString(), recordExperiences.ToString());
         float timeSinceBufferReset = Time.time - bufferResetTime;
         Monitor.Log("Seconds since buffer reset " + resetKey.ToString(), Mathf.FloorToInt(timeSinceBufferReset));
-	}
+    }
 
     void FixedUpdate()
     {

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

 
     //[HideInInspector]
     ///**<  \brief Keeps track of the agents which subscribe to this brain*/
-    //public Dictionary<int, Agent> agents = new Dictionary<int, Agent>();
+    // public Dictionary<int, Agent> agents = new Dictionary<int, Agent>();
 
     [SerializeField]
     ScriptableObject[] CoreBrains;
 
-    //Ensures the coreBrains are not dupplicated with the brains
+    // Ensures the coreBrains are not dupplicated with the brains
-	 * If the brain gameObject was just created, it generates a list of 
-	 * coreBrains (one for each brainType) */
+     * If the brain gameObject was just created, it generates a list of 
+     * coreBrains (one for each brainType) */
     public void UpdateCoreBrains()
     {
 

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

     public string logPath;
 
     /**< \brief The default reset parameters are sent via socket*/
-	public Dictionary<string, float> resetParameters;
+    public Dictionary<string, float> resetParameters;
 
     /**< \brief A list of the all the brains names sent via socket*/
     public List<string> brainNames;

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

     /// Uses the Decision Component to decide that action to take
     public void DecideAction(Dictionary<Agent, AgentInfo> agentInfo)
     {
-	    if (coord!=null)
-	    {
+        if (coord!=null)
+        {
-	    }
-		if (decision == null)
+        }
+
+        if (decision == null)
-
-			agent.UpdateVectorAction(decision.Decide(
+            agent.UpdateVectorAction(decision.Decide(
                 agentInfo[agent].stackedVectorObservation,
                 agentInfo[agent].visualObservations,
                 agentInfo[agent].reward,
         }
+
-				agentInfo[agent].stackedVectorObservation,
-				agentInfo[agent].visualObservations,
-				agentInfo[agent].reward,
-				agentInfo[agent].done,
-				agentInfo[agent].memories));
+                agentInfo[agent].stackedVectorObservation,
+                agentInfo[agent].visualObservations,
+                agentInfo[agent].reward,
+                agentInfo[agent].done,
+                agentInfo[agent].memories));
         }
     }
 
-#if UNITY_EDITOR
-        EditorGUILayout.LabelField("", GUI.skin.horizontalSlider);
-		broadcast = EditorGUILayout.Toggle(new GUIContent("Broadcast",
-					  "If checked, the brain will broadcast states and actions to Python."), broadcast);
-        if (brain.gameObject.GetComponent<Decision>() == null)
-        {
+        #if UNITY_EDITOR
+            EditorGUILayout.LabelField("", GUI.skin.horizontalSlider);
+            broadcast = EditorGUILayout.Toggle(new GUIContent("Broadcast",
+            "If checked, the brain will broadcast states and actions to Python."), broadcast);
+            if (brain.gameObject.GetComponent<Decision>() == null)
+            {
-        }
-#endif
+            }
+        #endif
     }
 
 }

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

     {
 #if ENABLE_TENSORFLOW
 #if UNITY_ANDROID
-		// This needs to ba called only once and will raise an exception if 
+        // This needs to ba called only once and will raise an exception if 
-		try{
-			TensorFlowSharp.Android.NativeBinding.Init();
-		}
-		catch{
-			
-		}
+        try{
+            TensorFlowSharp.Android.NativeBinding.Init();
+        }
+        catch{
+            
+        }
 #endif
         if ((communicator == null)
         || (!broadcast))
     public void DecideAction(Dictionary<Agent, AgentInfo> agentInfo)
     {
 #if ENABLE_TENSORFLOW
-		if (coord != null)
-		{
-			coord.GiveBrainInfo(brain, agentInfo);
-		}
+        if (coord != null)
+        {
+            coord.GiveBrainInfo(brain, agentInfo);
+        }
         int currentBatchSize = agentInfo.Count();
         List<Agent> agentList = agentInfo.Keys.ToList();
         if (currentBatchSize == 0)

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

         else
         {
             foreach (Agent agent in agentInfo.Keys)
-			{
+            {
                 var action = new float[1] { defaultAction };
                 foreach (DiscretePlayerAction dha in discretePlayerActions)
                 {

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

     /// \brief Implement this method to define the logic of decision making 
     /// for the CoreBrainHeuristic
     /** Given the information about the agent, return a vector of actions.
-	* @param state The state of the agent
-	* @param observation The cameras the agent uses
-	* @param reward The reward the agent had at the previous step
-	* @param done Weather or not the agent is done
-	* @param memory The memories stored from the previous step with MakeMemory()
-	* @return The vector of actions the agent will take at the next step
-	*/
+    * @param state The state of the agent
+    * @param observation The cameras the agent uses
+    * @param reward The reward the agent had at the previous step
+    * @param done Whether or not the agent is done
+    * @param memory The memories stored from the previous step with MakeMemory()
+    * @return The vector of actions the agent will take at the next step
+    */
-	* @param state The state of the agent
-	* @param observation The cameras the agent uses
-	* @param reward The reward the agent had at the previous step
-	* @param done Weather or not the agent is done
-	* @param memory The memories stored from the previous step with MakeMemory()
-	* @return The vector of memories the agent will use at the next step
-	*/
+    * @param state The state of the agent
+    * @param observation The cameras the agent uses
+    * @param reward The reward the agent had at the previous step
+    * @param done Weather or not the agent is done
+    * @param memory The memories stored from the previous step with MakeMemory()
+    * @return The vector of memories the agent will use at the next step
+    */
     List<float> MakeMemory(List<float> state, List<Texture2D> observation, float reward, bool done, List<float> memory);
 }

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

         sMessage.maxes = new List<bool>(defaultNumAgents);
         sMessage.textObservations = new List<string>(defaultNumAgents);
 
-        //Initialize the list of brains the Communicator must listen to
+        // Initialize the list of brains the Communicator must listen to
         // Issue : This assumes all brains are broadcasting.
         foreach (string k in accParamerters.brainNames)
         {

unity-environment/Assets/ML-Agents/Template/Scripts/TemplateAcademy.cs

 
 public class TemplateAcademy : Academy {
 
-	public override void AcademyReset()
-	{
+    public override void AcademyReset()
+    {
-	}
+    }
-	public override void AcademyStep()
-	{
+    public override void AcademyStep()
+    {
-	}
+    }
 
 }

unity-environment/Assets/ML-Agents/Template/Scripts/TemplateAgent.cs

 
 
 
-	public override void CollectObservations()
-	{
+    public override void CollectObservations()
+    {
-	}
+    }
-	}
+    }
-	public override void AgentReset()
-	{
+    public override void AgentReset()
+    {
-	}
+    }
-	public override void AgentOnDone()
-	{
+    public override void AgentOnDone()
+    {
-	}
+    }
 }

unity-environment/Assets/ML-Agents/Template/Scripts/TemplateDecision.cs

     public List<float> MakeMemory(List<float> state, List<Texture2D> observation, float reward, bool done, List<float> memory)
     {
         return new List<float>();
-		
+        
     }
 }

docs/Learning-Environment-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.

docs/Training-LSTM.md

+# Using Recurrent Neural Network in ML-Agents
+
+## What are memories for?
+Have you ever entered a room to get something and immediately forgot
+what you were looking for? Don't let that happen to 
+your agents.  
+
+It is now possible to give memories to your agents. When training, the 
+agents will be able to store a vector of floats to be used next time 
+they need to make a decision. 
+
+![Brain Inspector](images/ml-agents-LSTM.png)
+
+Deciding what the agents should remember in order to solve a task is not 
+easy to do by hand, but our training algorithms can learn to keep 
+track of what is important to remember with [LSTM](https://en.wikipedia.org/wiki/Long_short-term_memory). 
+
+## How to use
+When configuring the trainer parameters in the `trainer_config.yaml` 
+file, add the following parameters to the Brain you want to use.
+
+```
+	use_recurrent: true
+	sequence_length: 64
+	memory_size: 256
+```
+
+* `use_recurent` is a flag that notifies the  trainer that you want 
+to use a Recurrent Neural Network.
+* `sequence_length` defines how long the sequences of experiences 
+must be while training. In order to use a LSTM, training requires 
+a sequence of experiences instead of single experiences.
+* `memory_size` corresponds to the size of the memory the agent 
+must keep. Note that if this number is too small, the agent will not 
+be able to remember a lot of things. If this number is too large, 
+the neural network will take longer to train. 
+
+## Limitations
+* LSTM does not work well with continuous vector action space. 
+Please use discrete vector action space for better results.
+* Since the memories must be sent back and forth between python 
+and Unity, using too large `memory_size` will slow down training.
+* Adding a recurrent layer increases the complexity of the neural 
+network, it is recommended to decrease `num_layers` when using recurrent.
+* It is required that `memory_size` be divisible by 4.
+
+