Merge pull request #1931 from Unity-Technologies/release-v0.8

Release v0.8
6 年前 · 2d1bda57
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
          command: |
            python3 -m venv venv
            . venv/bin/activate
-            cd ml-agents && pip install -e .
+            cd ml-agents-envs && pip install -e .
+            cd ../ml-agents && pip install -e .
+            pip install pytest-cov==2.6.1 codacy-coverage==1.3.11
+            cd ../gym-unity && pip install -e .

      - save_cache:
          paths:
      - run:
-          name: Run Tests
+          name: Run Tests for ml-agents and gym_unity
-            cd ml-agents/ && pytest tests/
-            pytest --junitxml=test-reports/junit.xml
+            pytest --cov=mlagents --cov-report xml --junitxml=test-reports/junit.xml -p no:warnings
+            python-codacy-coverage -r coverage.xml
-          path: ml-agents/test-reports
+          path: test-reports
-          path: ml-agents/test-reports
-          destination: ml-agents/test-reports
+          path: test-reports
+          destination: test-reports
--- a/.gitignore
+++ b/.gitignore
 /UnitySDK/Assets/ML-Agents/Plugins/System.Numerics*
 /UnitySDK/Assets/ML-Agents/Plugins/System.ValueTuple*

+# Plugins
+/UnitySDK/Assets/ML-Agents/VideoRecorder*
+
 # Generated doc folders
 /docs/html

--- a/8
+++ b/8
 # xvfb is used to do CPU based rendering of Unity
 RUN apt-get install -y xvfb

+# Install ml-agents-envs package locally
+COPY ml-agents-envs /ml-agents-envs
+WORKDIR /ml-agents-envs
+RUN pip install -e .
+
+# Install ml-agents package next
-RUN pip install .
+RUN pip install -e .

 # port 5005 is the port used in in Editor training.
 EXPOSE 5005
--- a/README.md
+++ b/README.md
 <img src="docs/images/image-banner.png" align="middle" width="3000"/>

 # Unity ML-Agents Toolkit (Beta)
+[![docs badge](https://img.shields.io/badge/docs-reference-blue.svg)](docs/Readme.md)
+[![license badge](https://img.shields.io/badge/license-Apache--2.0-green.svg)](LICENSE)

 **The Unity Machine Learning Agents Toolkit** (ML-Agents) is an open-source
 Unity plugin that enables games and simulations to serve as environments for
 * Visualizing network outputs within the environment
 * Simplified set-up with Docker
 * Wrap learning environments as a gym
+* Utilizes the Unity Inference Engine
+* Train using concurrent Unity environment instances

 ## Documentation

--- a/UnitySDK/Assets/ML-Agents/Editor/AgentEditor.cs
+++ b/UnitySDK/Assets/ML-Agents/Editor/AgentEditor.cs
                "agentParameters.onDemandDecision");
            SerializedProperty cameras = serializedAgent.FindProperty(
                "agentParameters.agentCameras");
+            SerializedProperty renderTextures = serializedAgent.FindProperty(
+                "agentParameters.agentRenderTextures");
+            if (cameras.arraySize > 0 && renderTextures.arraySize > 0)
+            {
+                EditorGUILayout.HelpBox("Brain visual observations created by first getting all cameras then all render textures.", MessageType.Info);    
+            }
+            
            EditorGUILayout.LabelField("Agent Cameras");
            for (int i = 0; i < cameras.arraySize; i++)
            {
            }

            EditorGUILayout.EndHorizontal();
+            
+            EditorGUILayout.LabelField("Agent RenderTextures");
+            for (int i = 0; i < renderTextures.arraySize; i++)
+            {
+                EditorGUILayout.PropertyField(
+                    renderTextures.GetArrayElementAtIndex(i),
+                    new GUIContent("RenderTexture " + (i + 1).ToString() + ": "));
+            }
+
+            EditorGUILayout.BeginHorizontal();
+            if (GUILayout.Button("Add RenderTextures", EditorStyles.miniButton))
+            {
+                renderTextures.arraySize++;
+            }
+
+            if (GUILayout.Button("Remove RenderTextures", EditorStyles.miniButton))
+            {
+                renderTextures.arraySize--;
+            }
+
+            EditorGUILayout.EndHorizontal();
+

            EditorGUILayout.PropertyField(
                maxSteps,
--- a/UnitySDK/Assets/ML-Agents/Editor/BrainParametersDrawer.cs
+++ b/UnitySDK/Assets/ML-Agents/Editor/BrainParametersDrawer.cs
                property.FindPropertyRelative(NumVecObsPropName),
                new GUIContent("Stacked Vectors",
                    "Number of states that will be stacked before " +
-                    "beeing fed to the neural network."));
+                    "being fed to the neural network."));
            position.y += LineHeight;
            EditorGUI.indentLevel--; 
        }
--- a/UnitySDK/Assets/ML-Agents/Examples/3DBall/TFModels/3DBallHardLearning.nn
+++ b/UnitySDK/Assets/ML-Agents/Examples/3DBall/TFModels/3DBallHardLearning.nn
--- a/UnitySDK/Assets/ML-Agents/Examples/3DBall/TFModels/3DBallLearning.nn
+++ b/UnitySDK/Assets/ML-Agents/Examples/3DBall/TFModels/3DBallLearning.nn
--- a/UnitySDK/Assets/ML-Agents/Examples/BananaCollectors/Scripts/BananaAgent.cs
+++ b/UnitySDK/Assets/ML-Agents/Examples/BananaCollectors/Scripts/BananaAgent.cs
    public Material frozenMaterial;
    public GameObject myLaser;
    public bool contribute;
-    private RayPerception rayPer;
+    private RayPerception3D rayPer;
    public bool useVectorObs;

    public override void InitializeAgent()
        Monitor.verticalOffset = 1f;
        myArea = area.GetComponent<BananaArea>();
-        rayPer = GetComponent<RayPerception>();
+        rayPer = GetComponent<RayPerception3D>();
        myAcademy = FindObjectOfType<BananaAcademy>();
    }

        if (shoot)
        {
            myLaser.transform.localScale = new Vector3(1f, 1f, 1f);
-            Vector3 position = transform.TransformDirection(RayPerception.PolarToCartesian(25f, 90f));
+            Vector3 position = transform.TransformDirection(RayPerception3D.PolarToCartesian(25f, 90f));
            Debug.DrawRay(transform.position, position, Color.red, 0f, true);
            RaycastHit hit;
            if (Physics.SphereCast(transform.position, 2f, position, out hit, 25f))
--- a/UnitySDK/Assets/ML-Agents/Examples/BananaCollectors/TFModels/BananaLearning.nn
+++ b/UnitySDK/Assets/ML-Agents/Examples/BananaCollectors/TFModels/BananaLearning.nn
--- a/UnitySDK/Assets/ML-Agents/Examples/Basic/TFModels/BasicLearning.nn
+++ b/UnitySDK/Assets/ML-Agents/Examples/Basic/TFModels/BasicLearning.nn
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+++ b/UnitySDK/Assets/ML-Agents/Examples/Crawler/TFModels/CrawlerDynamicLearning.nn
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+++ b/UnitySDK/Assets/ML-Agents/Examples/Crawler/TFModels/CrawlerStaticLearning.nn
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--- a/UnitySDK/Assets/ML-Agents/Examples/GridWorld/Scenes/GridWorld.unity.meta
+++ b/UnitySDK/Assets/ML-Agents/Examples/GridWorld/Scenes/GridWorld.unity.meta
 fileFormatVersion: 2
-guid: 7c777442467e245108558a5155153927
-timeCreated: 1502222754
-licenseType: Free
+guid: c6fdbb551d80c435abe6fc911b1ea11f
+  externalObjects: {}
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--- a/UnitySDK/Assets/ML-Agents/Examples/GridWorld/Scripts/GridAgent.cs
+++ b/UnitySDK/Assets/ML-Agents/Examples/GridWorld/Scripts/GridAgent.cs
    public float timeBetweenDecisionsAtInference;
    private float timeSinceDecision;

+    [Tooltip("Because we want an observation right before making a decision, we can force " + 
+             "a camera to render before making a decision. Place the agentCam here if using " +
+             "RenderTexture as observations.")]
+    public Camera renderCamera;
+
    [Tooltip("Selecting will turn on action masking. Note that a model trained with action " +
             "masking turned on may not behave optimally when action masking is turned off.")]
    public bool maskActions = true;

    private void WaitTimeInference()
    {
+        if(renderCamera != null)
+        {
+            renderCamera.Render();
+        }
+
        if (!academy.GetIsInference())
        {
            RequestDecision();
--- a/UnitySDK/Assets/ML-Agents/Examples/GridWorld/TFModels/GridWorldLearning.nn
+++ b/UnitySDK/Assets/ML-Agents/Examples/GridWorld/TFModels/GridWorldLearning.nn
--- a/UnitySDK/Assets/ML-Agents/Examples/Hallway/TFModels/HallwayLearning.nn
+++ b/UnitySDK/Assets/ML-Agents/Examples/Hallway/TFModels/HallwayLearning.nn
--- a/UnitySDK/Assets/ML-Agents/Examples/PushBlock/TFModels/PushBlockLearning.nn
+++ b/UnitySDK/Assets/ML-Agents/Examples/PushBlock/TFModels/PushBlockLearning.nn
--- a/UnitySDK/Assets/ML-Agents/Examples/Pyramids/TFModels/PyramidsLearning.nn
+++ b/UnitySDK/Assets/ML-Agents/Examples/Pyramids/TFModels/PyramidsLearning.nn
--- a/UnitySDK/Assets/ML-Agents/Examples/Reacher/TFModels/ReacherLearning.nn
+++ b/UnitySDK/Assets/ML-Agents/Examples/Reacher/TFModels/ReacherLearning.nn
--- a/UnitySDK/Assets/ML-Agents/Examples/SharedAssets/Scripts/RayPerception.cs
+++ b/UnitySDK/Assets/ML-Agents/Examples/SharedAssets/Scripts/RayPerception.cs
-using System.Collections.Generic;
+using System.Collections;
+using System.Collections.Generic;
-namespace MLAgents
-{
-
-    /// <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;
-                }
+public abstract class RayPerception : MonoBehaviour {
-                perceptionBuffer.AddRange(subList);
-            }
-
-            return perceptionBuffer;
-        }
+	protected List<float> perceptionBuffer = new List<float>();
-        /// <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);
-        }
+	public virtual List<float> Perceive(float rayDistance,
+		float[] rayAngles, string[] detectableObjects,
+		float startOffset, float endOffset)
+	{
+		return perceptionBuffer;
+	}
-        /// <summary>
-        /// Converts degrees to radians.
-        /// </summary>
-        public static float DegreeToRadian(float degree)
-        {
-            return degree * Mathf.PI / 180f;
-        }
-    }
+	/// <summary>
+	/// Converts degrees to radians.
+	/// </summary>
+	public static float DegreeToRadian(float degree)
+	{
+		return degree * Mathf.PI / 180f;
+	}
+	
 }
--- a/UnitySDK/Assets/ML-Agents/Examples/SharedAssets/Scripts/RayPerception.cs.meta
+++ b/UnitySDK/Assets/ML-Agents/Examples/SharedAssets/Scripts/RayPerception.cs.meta
 fileFormatVersion: 2
-guid: bb172294dbbcc408286b156a2c4b553c
+guid: a14e2e238ae844231bc2c88e17bae5a5
 MonoImporter:
  externalObjects: {}
  serializedVersion: 2
--- a/UnitySDK/Assets/ML-Agents/Examples/Soccer/Scripts/AgentSoccer.cs
+++ b/UnitySDK/Assets/ML-Agents/Examples/Soccer/Scripts/AgentSoccer.cs
-using System.Collections;
-using System.Collections.Generic;
-using UnityEngine;
+using UnityEngine;
 using MLAgents;

 public class AgentSoccer : Agent
    {
-        red, blue
+        Red, 
+        Blue
-        striker, goalie
+        Striker, 
+        Goalie
+    
+    
-    public Rigidbody agentRB;
+    public Rigidbody agentRb;
    SoccerAcademy academy;
    Renderer agentRenderer;
    RayPerception rayPer;
        team = (Team)Random.Range(0, 2);
-        agentRenderer.material = team == Team.red ? academy.redMaterial : academy.blueMaterial;
+        if (team == Team.Red)
+        {
+            JoinRedTeam(agentRole);
+        }
+        else
+        {
+            JoinBlueTeam(agentRole);
+        }
-        team = Team.red;
+        team = Team.Red;
+        tag = "redAgent";
-        team = Team.blue;
+        team = Team.Blue;
+        tag = "blueAgent";
    }

    public override void InitializeAgent()
        rayPer = GetComponent<RayPerception>();
        academy = FindObjectOfType<SoccerAcademy>();
-        PlayerState playerState = new PlayerState();
-        playerState.agentRB = GetComponent<Rigidbody>();
-        agentRB = GetComponent<Rigidbody>();
-        agentRB.maxAngularVelocity = 500;
-        playerState.startingPos = transform.position;
-        playerState.agentScript = this;
+        agentRb = GetComponent<Rigidbody>();
+        agentRb.maxAngularVelocity = 500;
+
+        var playerState = new PlayerState
+        {
+            agentRB = agentRb, 
+            startingPos = transform.position, 
+            agentScript = this,
+        };
        area.playerStates.Add(playerState);
        playerIndex = area.playerStates.IndexOf(playerState);
        playerState.playerIndex = playerIndex;
        float rayDistance = 20f;
        float[] rayAngles = { 0f, 45f, 90f, 135f, 180f, 110f, 70f };
        string[] detectableObjects;
-        if (team == Team.red)
+        if (team == Team.Red)
-            detectableObjects = new string[] { "ball", "redGoal", "blueGoal",
+            detectableObjects = new[] { "ball", "redGoal", "blueGoal",
-            detectableObjects = new string[] { "ball", "blueGoal", "redGoal",
+            detectableObjects = new[] { "ball", "blueGoal", "redGoal",
                "wall", "blueAgent", "redAgent" };
        }
        AddVectorObs(rayPer.Perceive(rayDistance, rayAngles, detectableObjects, 0f, 0f));
        int action = Mathf.FloorToInt(act[0]);

        // Goalies and Strikers have slightly different action spaces.
-        if (agentRole == AgentRole.goalie)
+        if (agentRole == AgentRole.Goalie)
        {
            kickPower = 0f;
            switch (action)
            }
        }
        transform.Rotate(rotateDir, Time.deltaTime * 100f);
-        agentRB.AddForce(dirToGo * academy.agentRunSpeed,
+        agentRb.AddForce(dirToGo * academy.agentRunSpeed,
                         ForceMode.VelocityChange);

    }
    {
        // Existential penalty for strikers.
-        if (agentRole == AgentRole.striker)
+        if (agentRole == AgentRole.Striker)
-        if (agentRole == AgentRole.goalie)
+        if (agentRole == AgentRole.Goalie)
        {
            AddReward(1f / 3000f);
        }
    void OnCollisionEnter(Collision c)
    {
        float force = 2000f * kickPower;
-        if (c.gameObject.tag == "ball")
+        if (c.gameObject.CompareTag("ball"))
        {
            Vector3 dir = c.contacts[0].point - transform.position;
            dir = dir.normalized;

-
    public override void AgentReset()
    {
        if (academy.randomizePlayersTeamForTraining)

-        if (team == Team.red)
+        if (team == Team.Red)
        {
            JoinRedTeam(agentRole);
            transform.rotation = Quaternion.Euler(0f, -90f, 0f);
            JoinBlueTeam(agentRole);
            transform.rotation = Quaternion.Euler(0f, 90f, 0f);
        }
-        transform.position = area.GetRandomSpawnPos(team.ToString(),
-                                                    agentRole.ToString());
-        agentRB.velocity = Vector3.zero;
-        agentRB.angularVelocity = Vector3.zero;
+        transform.position = area.GetRandomSpawnPos(agentRole, team);
+        agentRb.velocity = Vector3.zero;
+        agentRb.angularVelocity = Vector3.zero;
-    }
-
-    public override void AgentOnDone()
-    {
-
    }
 }
--- a/UnitySDK/Assets/ML-Agents/Examples/Soccer/Scripts/SoccerBallController.cs
+++ b/UnitySDK/Assets/ML-Agents/Examples/Soccer/Scripts/SoccerBallController.cs
    {
        if (col.gameObject.CompareTag(redGoalTag)) //ball touched red goal
        {
-            area.GoalTouched(AgentSoccer.Team.blue);
+            area.GoalTouched(AgentSoccer.Team.Blue);
-            area.GoalTouched(AgentSoccer.Team.red);
+            area.GoalTouched(AgentSoccer.Team.Red);
        }
    }
 }
--- a/UnitySDK/Assets/ML-Agents/Examples/Soccer/Scripts/SoccerFieldArea.cs
+++ b/UnitySDK/Assets/ML-Agents/Examples/Soccer/Scripts/SoccerFieldArea.cs
                ps.agentScript.ChooseRandomTeam();
            }

-            if (scoredTeam == AgentSoccer.Team.red)
+            if (scoredTeam == AgentSoccer.Team.Red)
            {
                StartCoroutine(GoalScoredSwapGroundMaterial(academy.redMaterial, 1));
            }

    public void RewardOrPunishPlayer(PlayerState ps, float striker, float goalie)
    {
-        if (ps.agentScript.agentRole == AgentSoccer.AgentRole.striker)
+        if (ps.agentScript.agentRole == AgentSoccer.AgentRole.Striker)
-        if (ps.agentScript.agentRole == AgentSoccer.AgentRole.goalie)
+        if (ps.agentScript.agentRole == AgentSoccer.AgentRole.Goalie)
        {
            ps.agentScript.AddReward(goalie);
        }

-    public Vector3 GetRandomSpawnPos(string type, string position)
+    public Vector3 GetRandomSpawnPos(AgentSoccer.AgentRole role, AgentSoccer.Team team)
-        Vector3 randomSpawnPos = Vector3.zero;
-        if (type == "red")
+        if (role == AgentSoccer.AgentRole.Goalie)
-            if (position == "goalie")
-            {
-                xOffset = 13f;
-            }
-            if (position == "striker")
-            {
-                xOffset = 7f;
-            }
+            xOffset = 13f;
-        if (type == "blue")
+        if (role == AgentSoccer.AgentRole.Striker)
-            if (position == "goalie")
-            {
-                xOffset = -13f;
-            }
-            if (position == "striker")
-            {
-                xOffset = -7f;
-            }
+            xOffset = 7f;
-        randomSpawnPos = ground.transform.position + 
+        if (team == AgentSoccer.Team.Blue)
+        {
+            xOffset = xOffset * -1f;
+        }
+        var randomSpawnPos = ground.transform.position + 
                               new Vector3(xOffset, 0f, 0f) 
                               + (Random.insideUnitSphere * 2);
        randomSpawnPos.y = ground.transform.position.y + 2;
+    public Vector3 GetBallSpawnPosition()
+    {
+        var randomSpawnPos = ground.transform.position + 
+                             new Vector3(0f, 0f, 0f) 
+                             + (Random.insideUnitSphere * 2);
+        randomSpawnPos.y = ground.transform.position.y + 2;
+        return randomSpawnPos;
+    }
+
    void SpawnObjAtPos(GameObject obj, Vector3 pos)
    {
        obj.transform.position = pos;
    {
-        ball.transform.position = GetRandomSpawnPos("ball", "ball");
+        ball.transform.position = GetBallSpawnPosition();
        ballRB.velocity = Vector3.zero;
        ballRB.angularVelocity = Vector3.zero;
    }
--- a/UnitySDK/Assets/ML-Agents/Examples/Tennis/Scripts/TennisArea.cs
+++ b/UnitySDK/Assets/ML-Agents/Examples/Tennis/Scripts/TennisArea.cs
 using System.Collections;
 using System.Collections.Generic;
-using System.Security.AccessControl;
 using UnityEngine;

 public class TennisArea : MonoBehaviour {
--- a/UnitySDK/Assets/ML-Agents/Examples/Tennis/TFModels/TennisLearning.nn
+++ b/UnitySDK/Assets/ML-Agents/Examples/Tennis/TFModels/TennisLearning.nn
--- a/UnitySDK/Assets/ML-Agents/Examples/WallJump/TFModels/BigWallJumpLearning.nn
+++ b/UnitySDK/Assets/ML-Agents/Examples/WallJump/TFModels/BigWallJumpLearning.nn
--- a/UnitySDK/Assets/ML-Agents/Examples/WallJump/TFModels/SmallWallJumpLearning.nn
+++ b/UnitySDK/Assets/ML-Agents/Examples/WallJump/TFModels/SmallWallJumpLearning.nn
--- a/UnitySDK/Assets/ML-Agents/Scripts/Academy.cs
+++ b/UnitySDK/Assets/ML-Agents/Scripts/Academy.cs

 /**
 * Welcome to Unity Machine Learning Agents (ML-Agents).
- * 
+ *
 * The ML-Agents toolkit contains five entities: Academy, Brain, Agent, Communicator and
 * Python API. The academy, and all its brains and connected agents live within
 * a learning environment (herin called Environment), while the communicator
        [Tooltip("Frames per second (FPS) engine attempts to maintain.")]
        public int targetFrameRate;

-        /// Initializes a new instance of the 
+        /// Initializes a new instance of the
        /// <see cref="EnvironmentConfiguration"/> class.
        /// <param name="width">Width of environment window (pixels).</param>
        /// <param name="height">Height of environment window (pixels).</param>
    }

    /// <summary>
-    /// An Academy is where Agent objects go to train their behaviors. More 
+    /// An Academy is where Agent objects go to train their behaviors. More
-    /// in a scene is attached to one brain (a single brain may be attached to 
+    /// in a scene is attached to one brain (a single brain may be attached to
    /// multiple agents). Currently, this class is expected to be extended to
    /// implement the desired academy behavior.
    /// </summary>
             "docs/Learning-Environment-Design-Academy.md")]
    public abstract class Academy : MonoBehaviour
    {
-        [SerializeField] 
+        [SerializeField]
-        
-        private const string kApiVersion = "API-7";
+
+        private const string kApiVersion = "API-8";
-        /// Used to restore oringal value when deriving Academy modifies it 
+        /// Used to restore oringal value when deriving Academy modifies it
-        /// Used to restore oringal value when deriving Academy modifies it 
+        /// Used to restore oringal value when deriving Academy modifies it
-        
+
-        /// Used to restore oringal value when deriving Academy modifies it 
+        /// Used to restore oringal value when deriving Academy modifies it
        private float originalMaximumDeltaTime;

        // Fields provided in the Inspector
        /// <summary/>
        /// <remarks>
        /// Default reset parameters are specified in the academy Editor, and can
-        /// be modified when training with an external Brain by passinga config 
-        /// dictionary at reset. 
+        /// be modified when training with an external Brain by passinga config
+        /// dictionary at reset.
+        public CommunicatorObjects.CustomResetParameters customResetParameters;

        // Fields not provided in the Inspector.

        /// the same message is not used multiple times.
        private ulong lastCommunicatorMessageNumber;

-        /// If true, the Academy will use inference settings. This field is 
+        /// If true, the Academy will use inference settings. This field is
        /// initialized in <see cref="Awake"/> depending on the presence
        /// or absence of a communicator. Furthermore, it can be modified by an
        /// external Brain during reset via <see cref="SetIsInference"/>.
        /// current episode.
        bool maxStepReached;

-        /// The number of episodes completed by the environment. Incremented 
+        /// The number of episodes completed by the environment. Incremented
-        /// each time a step is taken in the environment. Is reset to 0 during 
+        /// each time a step is taken in the environment. Is reset to 0 during
+        /// The number of total number of steps completed during the whole simulation. Incremented
+        /// each time a step is taken in the environment.
+        int totalStepCount;
+
        /// Flag that indicates whether the inference/training mode of the
        /// environment was switched by the external Brain. This impacts the
        /// engine settings at the next environment step.
        // The Academy uses a series of events to communicate with agents and
        // brains to facilitate synchronization. More specifically, it ensure
        // that all the agents performs their steps in a consistent order (i.e. no
-        // agent can act based on a decision before another agent has had a chance 
+        // agent can act based on a decision before another agent has had a chance
-        // Signals to all the Brains at each environment step so they can decide 
+        // Signals to all the Brains at each environment step so they can decide
-        // Signals to all the agents at each environment step along with the 
+        // Signals to all the listeners that the academy is being destroyed
+        public event System.Action DestroyAction;
+
+        // Signals to all the agents at each environment step along with the
        // Academy's maxStepReached, done and stepCount values. The agents rely
        // on this event to update their own values of max step reached and done
        // in addition to aligning on the step count of the global episode.
-        // if their flag has been set to done (assuming the agent has requested a 
+        // if their flag has been set to done (assuming the agent has requested a
        // decision).
        public event System.Action AgentResetIfDone;

            originalGravity = Physics.gravity;
            originalFixedDeltaTime = Time.fixedDeltaTime;
            originalMaximumDeltaTime = Time.maximumDeltaTime;
-            
+
            InitializeAcademy();
            Communicator communicator = null;

            {
                brain.SetToControlledExternally();
            }
-            
+
            // Try to launch the communicator by usig the arguments passed at launch
            try
            {
                Random.InitState(pythonParameters.Seed);
                Application.logMessageReceived += HandleLog;
                logPath = Path.GetFullPath(".") + "/UnitySDK.log";
-                logWriter = new StreamWriter(logPath, false);
-                logWriter.WriteLine(System.DateTime.Now.ToString());
-                logWriter.WriteLine(" ");
-                logWriter.Close();
+                using (var fs = File.Open(logPath, FileMode.Append, FileAccess.Write, FileShare.ReadWrite))
+                {
+                    logWriter = new StreamWriter(fs);
+                    logWriter.WriteLine(System.DateTime.Now.ToString());
+                    logWriter.WriteLine(" ");
+                    logWriter.Close();
+                }
            }

            // If a communicator is enabled/provided, then we assume we are in

            BrainDecideAction += () => { };
+            DestroyAction += () => { };
            AgentSetStatus += (m, d, i) => { };
            AgentResetIfDone += () => { };
            AgentSendState += () => { };
                {
                    resetParameters[kv.Key] = kv.Value;
                }
+                customResetParameters = newResetParameters.CustomResetParameters;
-            logWriter = new StreamWriter(logPath, true);
-            logWriter.WriteLine(type.ToString());
-            logWriter.WriteLine(logString);
-            logWriter.WriteLine(stackTrace);
-            logWriter.Close();
+            using (var fs = File.Open(logPath, FileMode.Append, FileAccess.Write, FileShare.ReadWrite))
+            {
+                logWriter = new StreamWriter(fs);
+                logWriter.WriteLine(type.ToString());
+                logWriter.WriteLine(logString);
+                logWriter.WriteLine(stackTrace);
+                logWriter.Close();
+            }
        }

        /// <summary>
        }

        /// <summary>
-        /// Returns the current step counter (within the current epside).
+        /// Returns the current step counter (within the current episode).
-        /// Current episode number.
+        /// Current step count.
        /// </returns>
        public int GetStepCount()
        {
        /// <summary>
+        /// Returns the total step counter.
+        /// </summary>
+        /// <returns>
+        /// Total step count.
+        /// </returns>
+        public int GetTotalStepCount()
+        {
+            return totalStepCount;
+        }
+
+        /// <summary>
        /// Sets the done flag to true.
        /// </summary>
        public void Done()
        }

        /// <summary>
-        /// Forces the full reset. The done flags are not affected. Is either 
+        /// Forces the full reset. The done flags are not affected. Is either
        /// called the first reset at inference and every external reset
        /// at training.
        /// </summary>
            AgentAct();

            stepCount += 1;
+            totalStepCount += 1;
        }

        /// <summary>
            Physics.gravity = originalGravity;
            Time.fixedDeltaTime = originalFixedDeltaTime;
            Time.maximumDeltaTime = originalMaximumDeltaTime;
+
+            // Signal to listeners that the academy is being destroyed now
+            DestroyAction();
-}
+}
--- a/UnitySDK/Assets/ML-Agents/Scripts/Agent.cs
+++ b/UnitySDK/Assets/ML-Agents/Scripts/Agent.cs
        public int id;

        /// <summary>
+        /// User-customizable object for sending structured output from Unity to Python in response
+        /// to an action in addition to a scalar reward.
+        /// </summary>
+        public CustomObservation customObservation;
+
+        /// <summary>
        /// Converts a AgentInfo to a protobuffer generated AgentInfoProto
        /// </summary>
        /// <returns>The protobuf verison of the AgentInfo.</returns>
                MaxStepReached = maxStepReached,
                Done = done,
                Id = id,
+                CustomObservation = customObservation
            };
            if (memories != null)
            {
        public string textActions;
        public List<float> memories;
        public float value;
+        public CommunicatorObjects.CustomAction customAction;
    }

    /// <summary>
        /// observations.
        /// </summary>
        public List<Camera> agentCameras = new List<Camera>();
+        
+        /// <summary>
+        /// The list of the RenderTextures the agent uses for visual
+        /// observations.
+        /// </summary>
+        public List<RenderTexture> agentRenderTextures = new List<RenderTexture>();
+

        /// <summary>
        /// The maximum number of steps the agent takes before being done. 
        /// becomes enabled or active.
        void OnEnable()
        {
-            textureArray = new Texture2D[agentParameters.agentCameras.Count];
-            for (int i = 0; i < agentParameters.agentCameras.Count; i++)
+            var textureCount = agentParameters.agentCameras.Count+agentParameters.agentRenderTextures.Count;
+            textureArray = new Texture2D[textureCount];
+            for (int i = 0; i < textureCount; i++)
            {
                textureArray[i] = new Texture2D(1, 1, TextureFormat.RGB24, false);
            }
                          * param.numStackedVectorObservations]);

            info.visualObservations = new List<Texture2D>();
+            info.customObservation = null;
        }

        /// <summary>
            info.stackedVectorObservation.AddRange(info.vectorObservation);

            info.visualObservations.Clear();
-            if (param.cameraResolutions.Length > agentParameters.agentCameras.Count)
+            var visualObservationCount = agentParameters.agentCameras.Count+agentParameters.agentRenderTextures.Count;
+            if (param.cameraResolutions.Length > visualObservationCount)
-                    "Not enough cameras for agent {0} : Bain {1} expecting at " +
-                    "least {2} cameras but only {3} were present.",
+                    "Not enough cameras/renderTextures for agent {0} : Brain {1} expecting at " +
+                    "least {2} cameras/renderTextures but only {3} were present.",
-                    agentParameters.agentCameras.Count));
+                    visualObservationCount));
-            for (int i = 0; i < brain.brainParameters.cameraResolutions.Length; i++)
+            //First add all cameras
+            for (int i = 0; i < agentParameters.agentCameras.Count; i++)
+                    ref textureArray[i]);
+                info.visualObservations.Add(textureArray[i]);
+            }
+            
+            //Then add all renderTextures
+            var camCount = agentParameters.agentCameras.Count;
+            for (int i = 0; i < agentParameters.agentRenderTextures.Count; i++)
+            {
+                ObservationToTexture(
+                    agentParameters.agentRenderTextures[i],
+                    param.cameraResolutions[camCount+i].width,
+                    param.cameraResolutions[camCount+i].height,
                    ref textureArray[i]);
                info.visualObservations.Add(textureArray[i]);
            }
        /// <param name="actionIndex">The index of the masked action on branch 0</param>
        protected void SetActionMask(int actionIndex)
        {
-            actionMasker.SetActionMask(0, new int[1] {actionIndex});
+            actionMasker.SetActionMask(0, new int[1] { actionIndex });
        }

        /// <summary>
        /// <param name="actionIndex">The index of the masked action</param>
        protected void SetActionMask(int branch, int actionIndex)
        {
-            actionMasker.SetActionMask(branch, new int[1] {actionIndex});
+            actionMasker.SetActionMask(branch, new int[1] { actionIndex });
        }

        /// <summary>
        }

        /// <summary>
+        /// Specifies the agent behavior at every step based on the provided
+        /// action.
+        /// </summary>
+        /// <param name="vectorAction">
+        /// Vector action. Note that for discrete actions, the provided array
+        /// will be of length 1.
+        /// </param>
+        /// <param name="textAction">Text action.</param>
+        /// <param name="customAction">
+        /// A custom action, defined by the user as custom protobuffer message. Useful if the action is hard to encode
+        /// as either a flat vector or a single string.
+        /// </param>
+        public virtual void AgentAction(float[] vectorAction, string textAction, CommunicatorObjects.CustomAction customAction)
+        {
+            // We fall back to not using the custom action if the subclassed Agent doesn't override this method.
+            AgentAction(vectorAction, textAction);
+        }
+
+        /// <summary>
        /// Specifies the agent behavior when done and 
        /// <see cref="AgentParameters.resetOnDone"/> is false. This method can be
        /// used to remove the agent from the scene.
        }

        /// <summary>
+        /// Updates the custom action.
+        /// </summary>
+        /// <param name="customAction">Custom action.</param>
+        public void UpdateCustomAction(CommunicatorObjects.CustomAction customAction)
+        {
+            action.customAction = customAction;
+        }
+
+        /// <summary>
        /// Updates the value of the agent.
        /// </summary>
        /// <param name="textActions">Text actions.</param>
            if ((requestAction) && (brain != null))
            {
                requestAction = false;
-                AgentAction(action.vectorActions, action.textActions);
+                AgentAction(action.vectorActions, action.textActions, action.customAction);
            }

            if ((stepCount >= agentParameters.maxStep)
            RenderTexture.active = prevActiveRT;
            RenderTexture.ReleaseTemporary(tempRT);
        }
-    }
+        
+        /// <summary>
+        /// Converts a RenderTexture and correspinding resolution to a 2D texture.
+        /// </summary>
+        /// <returns>The 2D texture.</returns>
+        /// <param name="obsTexture">RenderTexture.</param>
+        /// <param name="width">Width of resulting 2D texture.</param>
+        /// <param name="height">Height of resulting 2D texture.</param>
+        /// <param name="texture2D">Texture2D to render to.</param>
+        public static void ObservationToTexture(RenderTexture obsTexture, int width, int height, ref Texture2D texture2D)
+        {
+            if (width != texture2D.width || height != texture2D.height)
+            {
+                texture2D.Resize(width, height);
+            }
+            
+            if(width != obsTexture.width || height != obsTexture.height)
+            {
+                throw new UnityAgentsException(string.Format(
+                    "RenderTexture {0} : width/height is {1}/{2} brain is expecting {3}/{4}.",
+                    obsTexture.name, obsTexture.width, obsTexture.height, width, height));
+            }
+
+            var prevActiveRT = RenderTexture.active;
+            RenderTexture.active = obsTexture;
+
+            texture2D.ReadPixels(new Rect(0, 0, texture2D.width, texture2D.height), 0, 0);
+            texture2D.Apply();
+            RenderTexture.active = prevActiveRT;
+        }
+
+        /// <summary>
+        /// Sets the custom observation for the agent for this episode.
+        /// </summary>
+        /// <param name="customObservation">New value of the agent's custom observation.</param>
+        public void SetCustomObservation(CustomObservation customObservation)
+        {
+            info.customObservation = customObservation;
+        }
+    }