Merge pull request #2833 from Unity-Technologies/release-0.11.0

Release 0.11.0

.circleci/config.yml

           keys:
           # Parameterize the cache so that different python versions can get different versions of the packages
           - v1-dependencies-py<< parameters.pyversion >>-{{ checksum "python_deps.txt" }}
-          # fallback to using the latest cache if no exact match is found
-          - v1-dependencies-py<< parameters.pyversion >>-
 
       - run:
           name: Install Dependencies
             . venv/bin/activate
             pre-commit run --hook-stage manual markdown-link-check --all-files
 
+  protobuf_generation_check:
+    docker:
+      - image: circleci/python:3.7.3
+    working_directory: ~/repo/
+
+    steps:
+      - checkout
+      - run:
+          name: Combine proto files for caching
+          command: cat protobuf-definitions/proto/mlagents/envs/communicator_objects/*.proto > /tmp/proto_deps.txt
+
+      - restore_cache:
+          keys:
+          - v1-protobuf-gen-dependencies-{{ checksum "/tmp/proto_deps.txt" }}
+          - v1-protobuf-gen-dependencies-
+
+      - run:
+          name: Install Dependencies
+          command: |
+            sudo apt-get install nuget
+            nuget install Grpc.Tools -Version 1.14.1 -OutputDirectory protobuf-definitions/
+            python3 -m venv venv
+            . venv/bin/activate
+            pip install --upgrade pip
+            pip install grpcio-tools==1.13.0  --progress-bar=off
+            pip install mypy-protobuf==1.16.0 --progress-bar=off
+      - save_cache:
+          paths:
+            - ./venv
+          key: v1-protobuf-gen-dependencies-{{ checksum "/tmp/proto_deps.txt" }}
+
+      - run:
+          name: Generate protobufs
+          command: |
+            . venv/bin/activate
+            cd protobuf-definitions
+            chmod +x Grpc.Tools.1.14.1/tools/linux_x64/protoc
+            chmod +x Grpc.Tools.1.14.1/tools/linux_x64/grpc_csharp_plugin
+            COMPILER=Grpc.Tools.1.14.1/tools/linux_x64 ./make.sh
+            CS_PROTO_PATH=UnitySDK/Assets/ML-Agents/Scripts/Grpc/CommunicatorObjects
+            git diff --exit-code --quiet -- :/ ":(exclude,top)$CS_PROTO_PATH/*.meta" \
+              || { GIT_ERR=$?; echo "protobufs need to be regenerated, apply the patch uploaded to artifacts."; \
+              echo "Apply the patch with the command: git apply proto.patch"; \
+              git diff -- :/ ":(exclude,top)$CS_PROTO_PATH/*.meta" > /tmp/proto.patch; \
+              exit $GIT_ERR; }
+      - store_artifacts:
+            path: /tmp/proto.patch
+            destination: proto.patch
+
+  deploy:
+    parameters:
+      directory:
+        type: string
+        description: Local directory to use for publishing (e.g. ml-agents)
+    docker:
+      - image: circleci/python:3.6
+    steps:
+      - checkout
+      - run:
+          name: install python dependencies
+          command: |
+            python3 -m venv venv
+            . venv/bin/activate
+            pip install  --upgrade pip
+            pip install setuptools wheel twine
+      - run:
+          name: verify git tag vs. version
+          command: |
+            python3 -m venv venv
+            . venv/bin/activate
+            cd << parameters.directory >>
+            python setup.py verify
+      - run:
+          name: create packages
+          command: |
+            . venv/bin/activate
+            cd << parameters.directory >>
+            python setup.py sdist
+            python setup.py bdist_wheel
+      - run:
+          name: upload to pypi
+          # To upload to test, just add the following flag to twine upload:
+          # --repository-url https://test.pypi.org/legacy/
+          # and change the username to "mlagents-test"
+          command: |
+            . venv/bin/activate
+            cd << parameters.directory >>
+            twine upload -u mlagents -p $PYPI_PASSWORD dist/*
+
+  version: 2
   workflow:
     jobs:
       - build_python:
           # Test python 3.7 with the newest supported versions
           pip_constraints: test_constraints_max_version.txt
       - markdown_link_check
+      - protobuf_generation_check
+      - deploy:
+          name: deploy ml-agents-envs
+          directory: ml-agents-envs
+          filters:
+            tags:
+              only: /[0-9]+(\.[0-9]+)*(\.dev[0-9]+)*/
+            branches:
+              ignore: /.*/
+      - deploy:
+          name: deploy ml-agents
+          directory: ml-agents
+          filters:
+            tags:
+              only: /[0-9]+(\.[0-9]+)*(\.dev[0-9]+)*/
+            branches:
+              ignore: /.*/
+      - deploy:
+          name: deploy gym-unity
+          directory: gym-unity
+          filters:
+            tags:
+              only: /[0-9]+(\.[0-9]+)*(\.dev[0-9]+)*/
+            branches:
+              ignore: /.*/

.pre-commit-config.yaml

     rev: 19.3b0
     hooks:
     -   id: black
+        exclude: >
+            (?x)^(
+                .*_pb2.py|
+                .*_pb2_grpc.py
+            )$
+
 -   repo: https://github.com/pre-commit/mirrors-mypy
     rev: v0.720
     hooks:
         name: mypy-gym-unity
         files: "gym-unity/.*"
         args: [--ignore-missing-imports, --disallow-incomplete-defs]
+
 -   repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v2.2.3
     hooks:
                 .*cs.meta|
-                .*.css
+                .*.css|
+                .*.meta
             )$
         args: [--fix=lf]
     -   id: flake8
                 .*_pb2_grpc.py
             )$
+        additional_dependencies: [flake8-comprehensions]
+    -   id: trailing-whitespace
+        name: trailing-whitespace-markdown
+        types: [markdown]
+
+-   repo: https://github.com/pre-commit/pygrep-hooks
+    rev: v1.4.1  # Use the ref you want to point at
+    hooks:
+    -   id: python-check-mock-methods
+
 # "Local" hooks, see https://pre-commit.com/#repository-local-hooks
 -   repo: local
     hooks:
         exclude: ".*localized.*"
         # Only run manually, e.g. pre-commit run --hook-stage manual markdown-link-check
         stages: [manual]
+    -   id: validate-versions
+        name: validate library versions
+        language: script
+        entry: utils/validate_versions.py
+        files: ".*/setup.py"

README.md

 * Support for multiple environment configurations and training scenarios
 * Train memory-enhanced agents using deep reinforcement learning
 * Easily definable Curriculum Learning and Generalization scenarios
-* Broadcasting of agent behavior for supervised learning
 * Built-in support for Imitation Learning
 * Flexible agent control with On Demand Decision Making
 * Visualizing network outputs within the environment

SURVEY.md

 # Unity ML-Agents Toolkit Survey
 
-Your opinion matters a great deal to us. Only by hearing your thoughts on the Unity ML-Agents Toolkit can we continue to improve and grow. Please take a few minutes to let us know about it. 
+Your opinion matters a great deal to us. Only by hearing your thoughts on the Unity ML-Agents Toolkit can we continue to improve and grow. Please take a few minutes to let us know about it.
-[Fill out the survey](https://goo.gl/forms/qFMYSYr5TlINvG6f1) 
+[Fill out the survey](https://goo.gl/forms/qFMYSYr5TlINvG6f1)

UnitySDK/Assets/ML-Agents/Editor/AgentEditor.cs

 using UnityEngine;
 using UnityEditor;
+using Barracuda;
-/*
- This code is meant to modify the behavior of the inspector on Brain Components.
- Depending on the type of brain that is used, the available fields will be modified in the inspector accordingly.
-*/
+    /*
+     This code is meant to modify the behavior of the inspector on Agent Components.
+    */
     [CustomEditor(typeof(Agent), true)]
     [CanEditMultipleObjects]
     public class AgentEditor : Editor
             var serializedAgent = serializedObject;
             serializedAgent.Update();
 
-            var brain = serializedAgent.FindProperty("brain");
             var actionsPerDecision = serializedAgent.FindProperty(
                 "agentParameters.numberOfActionsBetweenDecisions");
             var maxSteps = serializedAgent.FindProperty(
             var isOdd = serializedAgent.FindProperty(
                 "agentParameters.onDemandDecision");
-            var cameras = serializedAgent.FindProperty(
-                "agentParameters.agentCameras");
-            var renderTextures = serializedAgent.FindProperty(
-                "agentParameters.agentRenderTextures");
-            EditorGUILayout.PropertyField(brain);
-
-            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 (var i = 0; i < cameras.arraySize; i++)
-            {
-                EditorGUILayout.PropertyField(
-                    cameras.GetArrayElementAtIndex(i),
-                    new GUIContent("Camera " + (i + 1) + ": "));
-            }
-
-            EditorGUILayout.BeginHorizontal();
-            if (GUILayout.Button("Add Camera", EditorStyles.miniButton))
-            {
-                cameras.arraySize++;
-            }
-
-            if (GUILayout.Button("Remove Camera", EditorStyles.miniButton))
-            {
-                cameras.arraySize--;
-            }
-
-            EditorGUILayout.EndHorizontal();
-
-            EditorGUILayout.LabelField("Agent RenderTextures");
-            for (var i = 0; i < renderTextures.arraySize; i++)
-            {
-                EditorGUILayout.PropertyField(
-                    renderTextures.GetArrayElementAtIndex(i),
-                    new GUIContent("RenderTexture " + (i + 1) + ": "));
-            }
-
-            EditorGUILayout.BeginHorizontal();
-            if (GUILayout.Button("Add RenderTextures", EditorStyles.miniButton))
-            {
-                renderTextures.arraySize++;
-            }
-
-            if (GUILayout.Button("Remove RenderTextures", EditorStyles.miniButton))
-            {
-                renderTextures.arraySize--;
-            }
-
-            EditorGUILayout.EndHorizontal();
 
 
             EditorGUILayout.PropertyField(

UnitySDK/Assets/ML-Agents/Editor/BrainParametersDrawer.cs

         // The height of a line in the Unity Inspectors
         private const float k_LineHeight = 17f;
         private const int k_VecObsNumLine = 3;
-        private const string k_CamResPropName = "cameraResolutions";
-        private const string k_CamWidthPropName = "width";
-        private const string k_CamHeightPropName = "height";
-        private const string k_CamGrayPropName = "blackAndWhite";
-        private const int k_DefaultCameraWidth = 84;
-        private const int k_DefaultCameraHeight = 84;
-        private const bool k_DefaultCameraGray = false;
-            if (property.isExpanded)
-            {
-                return k_LineHeight +
-                    GetHeightDrawVectorObservation() +
-                    GetHeightDrawVisualObservation(property) +
-                    GetHeightDrawVectorAction(property) +
-                    GetHeightDrawVectorActionDescriptions(property);
-            }
-            return k_LineHeight;
+            return GetHeightDrawVectorObservation() +
+                GetHeightDrawVectorAction(property);
+
+
-            property.isExpanded = EditorGUI.Foldout(position, property.isExpanded, label);
-            position.y += k_LineHeight;
-            if (property.isExpanded)
-            {
-                EditorGUI.BeginProperty(position, label, property);
-                EditorGUI.indentLevel++;
-
-                // Vector Observations
-                DrawVectorObservation(position, property);
-                position.y += GetHeightDrawVectorObservation();
+            EditorGUI.BeginProperty(position, label, property);
+            EditorGUI.indentLevel++;
-                //Visual Observations
-                DrawVisualObservations(position, property);
-                position.y += GetHeightDrawVisualObservation(property);
+            // Vector Observations
+            DrawVectorObservation(position, property);
+            position.y += GetHeightDrawVectorObservation();
-                // Vector Action
-                DrawVectorAction(position, property);
-                position.y += GetHeightDrawVectorAction(property);
+            // Vector Action
+            DrawVectorAction(position, property);
+            position.y += GetHeightDrawVectorAction(property);
-                // Vector Action Descriptions
-                DrawVectorActionDescriptions(position, property);
-                position.y += GetHeightDrawVectorActionDescriptions(property);
-                EditorGUI.EndProperty();
-            }
+            EditorGUI.EndProperty();
             EditorGUI.indentLevel = indent;
         }
 
         }
 
         /// <summary>
-        /// Draws the Visual Observations parameters for the Brain Parameters
-        /// </summary>
-        /// <param name="position">Rectangle on the screen to use for the property GUI.</param>
-        /// <param name="property">The SerializedProperty of the BrainParameters
-        /// to make the custom GUI for.</param>
-        private static void DrawVisualObservations(Rect position, SerializedProperty property)
-        {
-            EditorGUI.LabelField(position, "Visual Observations");
-            position.y += k_LineHeight;
-            var quarter = position.width / 4;
-            var resolutions = property.FindPropertyRelative(k_CamResPropName);
-            DrawVisualObsButtons(position, resolutions);
-            position.y += k_LineHeight;
-
-            // Display the labels for the columns : Index, Width, Height and Gray
-            var indexRect = new Rect(position.x, position.y, quarter, position.height);
-            var widthRect = new Rect(position.x + quarter, position.y, quarter, position.height);
-            var heightRect = new Rect(position.x + 2 * quarter, position.y, quarter, position.height);
-            var bwRect = new Rect(position.x + 3 * quarter, position.y, quarter, position.height);
-            EditorGUI.indentLevel++;
-            if (resolutions.arraySize > 0)
-            {
-                EditorGUI.LabelField(indexRect, "Index");
-                indexRect.y += k_LineHeight;
-                EditorGUI.LabelField(widthRect, "Width");
-                widthRect.y += k_LineHeight;
-                EditorGUI.LabelField(heightRect, "Height");
-                heightRect.y += k_LineHeight;
-                EditorGUI.LabelField(bwRect, "Gray");
-                bwRect.y += k_LineHeight;
-            }
-
-            // Iterate over the resolutions
-            for (var i = 0; i < resolutions.arraySize; i++)
-            {
-                EditorGUI.LabelField(indexRect, "Obs " + i);
-                indexRect.y += k_LineHeight;
-                var res = resolutions.GetArrayElementAtIndex(i);
-                var w = res.FindPropertyRelative("width");
-                w.intValue = EditorGUI.IntField(widthRect, w.intValue);
-                widthRect.y += k_LineHeight;
-                var h = res.FindPropertyRelative("height");
-                h.intValue = EditorGUI.IntField(heightRect, h.intValue);
-                heightRect.y += k_LineHeight;
-                var bw = res.FindPropertyRelative("blackAndWhite");
-                bw.boolValue = EditorGUI.Toggle(bwRect, bw.boolValue);
-                bwRect.y += k_LineHeight;
-            }
-            EditorGUI.indentLevel--;
-        }
-
-        /// <summary>
-        /// Draws the buttons to add and remove the visual observations parameters
-        /// </summary>
-        /// <param name="position">Rectangle on the screen to use for the property GUI.</param>
-        /// <param name="resolutions">The SerializedProperty of the resolution array
-        /// to make the custom GUI for.</param>
-        private static void DrawVisualObsButtons(Rect position, SerializedProperty resolutions)
-        {
-            var widthEighth = position.width / 8;
-            var addButtonRect = new Rect(position.x + widthEighth, position.y,
-                3 * widthEighth, position.height);
-            var removeButtonRect = new Rect(position.x + 4 * widthEighth, position.y,
-                3 * widthEighth, position.height);
-            if (resolutions.arraySize == 0)
-            {
-                addButtonRect.width *= 2;
-            }
-            // Display the buttons
-            if (GUI.Button(addButtonRect, "Add New", EditorStyles.miniButton))
-            {
-                resolutions.arraySize += 1;
-                var newRes = resolutions.GetArrayElementAtIndex(resolutions.arraySize - 1);
-                newRes.FindPropertyRelative(k_CamWidthPropName).intValue = k_DefaultCameraWidth;
-                newRes.FindPropertyRelative(k_CamHeightPropName).intValue = k_DefaultCameraHeight;
-                newRes.FindPropertyRelative(k_CamGrayPropName).boolValue = k_DefaultCameraGray;
-            }
-            if (resolutions.arraySize > 0)
-            {
-                if (GUI.Button(removeButtonRect, "Remove Last", EditorStyles.miniButton))
-                {
-                    resolutions.arraySize -= 1;
-                }
-            }
-        }
-
-        /// <summary>
-        /// The Height required to draw the Visual Observations parameters
-        /// </summary>
-        /// <returns>The height of the drawer of the Visual Observations </returns>
-        private static float GetHeightDrawVisualObservation(SerializedProperty property)
-        {
-            var visObsSize = property.FindPropertyRelative(k_CamResPropName).arraySize + 2;
-            if (property.FindPropertyRelative(k_CamResPropName).arraySize > 0)
-            {
-                visObsSize += 1;
-            }
-            return k_LineHeight * visObsSize;
-        }
-
-        /// <summary>
         /// Draws the Vector Actions parameters for the Brain Parameters
         /// </summary>
         /// <param name="position">Rectangle on the screen to use for the property GUI.</param>
                 actionSize += 1;
             }
             return actionSize * k_LineHeight;
-        }
-
-        /// <summary>
-        /// Draws the Vector Actions descriptions for the Brain Parameters
-        /// </summary>
-        /// <param name="position">Rectangle on the screen to use for the property GUI.</param>
-        /// <param name="property">The SerializedProperty of the BrainParameters
-        /// to make the custom GUI for.</param>
-        private static void DrawVectorActionDescriptions(Rect position, SerializedProperty property)
-        {
-            var bpVectorActionType = property.FindPropertyRelative(k_ActionTypePropName);
-            var vecActionSize = property.FindPropertyRelative(k_ActionSizePropName);
-            var numberOfDescriptions = 0;
-            if (bpVectorActionType.enumValueIndex == 1)
-            {
-                numberOfDescriptions = vecActionSize.GetArrayElementAtIndex(0).intValue;
-            }
-            else
-            {
-                numberOfDescriptions = vecActionSize.arraySize;
-            }
-
-            EditorGUI.indentLevel++;
-            var vecActionDescriptions =
-                property.FindPropertyRelative(k_ActionDescriptionPropName);
-            vecActionDescriptions.arraySize = numberOfDescriptions;
-            if (bpVectorActionType.enumValueIndex == 1)
-            {
-                //Continuous case :
-                EditorGUI.PropertyField(
-                    position,
-                    vecActionDescriptions,
-                    new GUIContent("Action Descriptions",
-                        "A list of strings used to name the available actionsm for the Brain."),
-                    true);
-                position.y += k_LineHeight;
-            }
-            else
-            {
-                // Discrete case :
-                EditorGUI.PropertyField(
-                    position,
-                    vecActionDescriptions,
-                    new GUIContent("Branch Descriptions",
-                        "A list of strings used to name the available branches for the Brain."),
-                    true);
-                position.y += k_LineHeight;
-            }
-        }
-
-        /// <summary>
-        /// The Height required to draw the Action Descriptions
-        /// </summary>
-        /// <returns>The height of the drawer of the Action Descriptions </returns>
-        private static float GetHeightDrawVectorActionDescriptions(SerializedProperty property)
-        {
-            var descriptionSize = 1;
-            if (property.FindPropertyRelative(k_ActionDescriptionPropName).isExpanded)
-            {
-                var descriptions = property.FindPropertyRelative(k_ActionDescriptionPropName);
-                descriptionSize += descriptions.arraySize + 1;
-            }
-            return descriptionSize * k_LineHeight;
         }
     }
 }

UnitySDK/Assets/ML-Agents/Editor/DemonstrationDrawer.cs

         return actionLabel.ToString();
     }
 
-    /// <summary>
-    /// Constructs complex label for each CameraResolution object.
-    /// An example of this could be `[ 84 X 84 ]`
-    /// for a single camera with 84 pixels height and width.
-    /// </summary>
-    private static string BuildCameraResolutionLabel(SerializedProperty cameraArray)
-    {
-        var numCameras = cameraArray.arraySize;
-        var cameraLabel = new StringBuilder("[ ");
-        for (var i = 0; i < numCameras; i++)
-        {
-            var camHeightPropName =
-                cameraArray.GetArrayElementAtIndex(i).FindPropertyRelative("height");
-            cameraLabel.Append(camHeightPropName.intValue);
-            cameraLabel.Append(" X ");
-            var camWidthPropName =
-                cameraArray.GetArrayElementAtIndex(i).FindPropertyRelative("width");
-            cameraLabel.Append(camWidthPropName.intValue);
-            if (i < numCameras - 1)
-            {
-                cameraLabel.Append(", ");
-            }
-        }
-
-        cameraLabel.Append(" ]");
-        return cameraLabel.ToString();
-    }
 
     /// <summary>
     /// Renders Inspector UI for Brain Parameters of Demonstration.
         var vecObsSizeProp = property.FindPropertyRelative("vectorObservationSize");
         var numStackedProp = property.FindPropertyRelative("numStackedVectorObservations");
         var actSizeProperty = property.FindPropertyRelative("vectorActionSize");
-        var camResProp = property.FindPropertyRelative("cameraResolutions");
         var actSpaceTypeProp = property.FindPropertyRelative("vectorActionSpaceType");
 
         var vecObsSizeLabel = vecObsSizeProp.displayName + ": " + vecObsSizeProp.intValue;
-        var camResLabel = camResProp.displayName + ": " + BuildCameraResolutionLabel(camResProp);
         var actSpaceTypeLabel = actSpaceTypeProp.displayName + ": " +
             (SpaceType)actSpaceTypeProp.enumValueIndex;
 
-        EditorGUILayout.LabelField(camResLabel);
         EditorGUILayout.LabelField(actSpaceTypeLabel);
     }
 

UnitySDK/Assets/ML-Agents/Editor/DemonstrationImporter.cs

                 Stream reader = File.OpenRead(ctx.assetPath);
 
                 var metaDataProto = DemonstrationMetaProto.Parser.ParseDelimitedFrom(reader);
-                var metaData = new DemonstrationMetaData(metaDataProto);
+                var metaData = metaDataProto.ToDemonstrationMetaData();
-                var brainParameters = new BrainParameters(brainParamsProto);
+                var brainParameters = brainParamsProto.ToBrainParameters();
 
                 reader.Close();
 

UnitySDK/Assets/ML-Agents/Editor/Tests/DemonstrationTests.cs

             {
                 vectorObservationSize = 3,
                 numStackedVectorObservations = 2,
-                cameraResolutions = new[] {new Resolution()},
                 vectorActionDescriptions = new[] {"TestActionA", "TestActionB"},
                 vectorActionSize = new[] {2, 2},
                 vectorActionSpaceType = SpaceType.Discrete
             var agentInfo = new AgentInfo
             {
                 reward = 1f,
-                visualObservations = new List<Texture2D>(),
                 actionMasks = new[] {false, true},
                 done = true,
                 id = 5,

UnitySDK/Assets/ML-Agents/Editor/Tests/EditModeTestInternalBrainTensorApplier.cs

 using System.Collections.Generic;
-using System.Linq;
 using NUnit.Framework;
 using UnityEngine;
 using System.Reflection;
             }
         }
 
-        private Dictionary<Agent, AgentInfo> GetFakeAgentInfos()
+        private List<Agent> GetFakeAgentInfos()
-            var infoA = new AgentInfo();
-            var infoB = new AgentInfo();
-            return new Dictionary<Agent, AgentInfo>(){{agentA, infoA}, {agentB, infoB}};
+            return new List<Agent> {agentA, agentB};
         }
 
         [Test]
 
             var applier = new ContinuousActionOutputApplier();
             applier.Apply(inputTensor, agentInfos);
-            var agents = agentInfos.Keys.ToList();
+            var agents = agentInfos;
 
             var agent = agents[0] as TestAgent;
             Assert.NotNull(agent);
             var alloc = new TensorCachingAllocator();
             var applier = new DiscreteActionOutputApplier(new[] {2, 3}, 0, alloc);
             applier.Apply(inputTensor, agentInfos);
-            var agents = agentInfos.Keys.ToList();
+            var agents = agentInfos;
 
             var agent = agents[0] as TestAgent;
             Assert.NotNull(agent);
 
             var applier = new MemoryOutputApplier();
             applier.Apply(inputTensor, agentInfos);
-            var agents = agentInfos.Keys.ToList();
+            var agents = agentInfos;
 
             var agent = agents[0] as TestAgent;
             Assert.NotNull(agent);
 
             var applier = new ValueEstimateApplier();
             applier.Apply(inputTensor, agentInfos);
-            var agents = agentInfos.Keys.ToList();
+            var agents = agentInfos;
 
             var agent = agents[0] as TestAgent;
             Assert.NotNull(agent);

UnitySDK/Assets/ML-Agents/Editor/Tests/EditModeTestInternalBrainTensorGenerator.cs

 {
     public class EditModeTestInternalBrainTensorGenerator
     {
-        private class TestAgent : Agent
-        {
-        }
-
-        private Dictionary<Agent, AgentInfo> GetFakeAgentInfos()
+        private static IEnumerable<Agent> GetFakeAgentInfos()
-            var infoA = new AgentInfo()
+            var infoA = new AgentInfo
-                stackedVectorObservation = (new[] {1f, 2f, 3f}).ToList(),
+                stackedVectorObservation = new[] { 1f, 2f, 3f }.ToList(),
-                storedVectorActions = new[] {1f, 2f},
-                actionMasks = null,
+                storedVectorActions = new[] { 1f, 2f },
+                actionMasks = null
-            var infoB = new AgentInfo()
+            var infoB = new AgentInfo
-                stackedVectorObservation = (new[] {4f, 5f, 6f}).ToList(),
-                memories = (new[] {1f, 1f, 1f}).ToList(),
-                storedVectorActions = new[] {3f, 4f},
-                actionMasks = new[] {true, false, false, false, false},
+                stackedVectorObservation = new[] { 4f, 5f, 6f }.ToList(),
+                memories = new[] { 1f, 1f, 1f }.ToList(),
+                storedVectorActions = new[] { 3f, 4f },
+                actionMasks = new[] { true, false, false, false, false },
+            agentA.Info = infoA;
+            agentB.Info = infoB;
-            return new Dictionary<Agent, AgentInfo>(){{agentA, infoA}, {agentB, infoB}};
+            return new List<Agent> { agentA, agentB };
         }
 
         [Test]
         [Test]
         public void GenerateVectorObservation()
         {
-            var inputTensor = new TensorProxy()
+            var inputTensor = new TensorProxy
-                shape = new long[] {2, 3}
+                shape = new long[] { 2, 3 }
             };
             const int batchSize = 4;
             var agentInfos = GetFakeAgentInfos();
         [Test]
         public void GenerateRecurrentInput()
         {
-            var inputTensor = new TensorProxy()
+            var inputTensor = new TensorProxy
-                shape = new long[] {2, 5}
+                shape = new long[] { 2, 5 }
             };
             const int batchSize = 4;
             var agentInfos = GetFakeAgentInfos();
         [Test]
         public void GeneratePreviousActionInput()
         {
-            var inputTensor = new TensorProxy()
+            var inputTensor = new TensorProxy
-                shape = new long[] {2, 2},
+                shape = new long[] { 2, 2 },
                 valueType = TensorProxy.TensorType.Integer
             };
             const int batchSize = 4;
         [Test]
         public void GenerateActionMaskInput()
         {
-            var inputTensor = new TensorProxy()
+            var inputTensor = new TensorProxy
-                shape = new long[] {2, 5},
+                shape = new long[] { 2, 5 },
                 valueType = TensorProxy.TensorType.FloatingPoint
             };
             const int batchSize = 4;

UnitySDK/Assets/ML-Agents/Editor/Tests/MLAgentsEditModeTest.cs

 using UnityEngine;
 using NUnit.Framework;
 using System.Reflection;
+using MLAgents.Sensor;
+using MLAgents.InferenceBrain;
 
 namespace MLAgents.Tests
 {
 
         public override void AcademyReset()
         {
-
         }
 
         public override void AcademyStep()
         public override void InitializeAgent()
         {
             initializeAgentCalls += 1;
+
+            // Add in some custom sensors so we can confirm they get sorted as expected.
+            var sensor1 = new TestSensor("testsensor1");
+            var sensor2 = new TestSensor("testsensor2");
+
+            m_Sensors.Add(sensor2);
+            m_Sensors.Add(sensor1);
+            AddVectorObs(0f);
         }
 
         public override void AgentAction(float[] vectorAction, string textAction)
         {
             agentOnDoneCalls += 1;
         }
+
+        public override float[] Heuristic()
+        {
+            return new float[0];
+        }
-    // This is an empty class for testing the behavior of agents and academy
-    // It is left empty because we are not testing any brain behavior
-    public class TestBrain : Brain
+    public class TestSensor : ISensor
-        public int numberOfCallsToInitialize;
-        public int numberOfCallsToDecideAction;
-        public static TestBrain Instantiate()
+        public string sensorName;
+
+        public TestSensor(string n)
-            return CreateInstance<TestBrain>();
+            sensorName = n;
-        protected override void Initialize()
+        public int[] GetFloatObservationShape()
-            numberOfCallsToInitialize++;
+            return new[] { 1 };
-        protected override void DecideAction()
+        public void WriteToTensor(TensorProxy tensorProxy, int agentIndex) { }
+
+        public byte[] GetCompressedObservation()
-            numberOfCallsToDecideAction++;
-            m_AgentInfos.Clear();
+            return null;
-    }
+        public SensorCompressionType GetCompressionType()
+        {
+            return SensorCompressionType.None;
+        }
+
+        public string GetName()
+        {
+            return sensorName;
+        }
+    }
 
     public class EditModeTestGeneration
     {
             var acaGo = new GameObject("TestAcademy");
             acaGo.AddComponent<TestAcademy>();
             var aca = acaGo.GetComponent<TestAcademy>();
+            aca.resetParameters = new ResetParameters();
             Assert.AreEqual(0, aca.initializeAcademyCalls);
             Assert.AreEqual(0, aca.GetStepCount());
             Assert.AreEqual(0, aca.GetEpisodeCount());
             var acaGo = new GameObject("TestAcademy");
             acaGo.AddComponent<TestAcademy>();
             var aca = acaGo.GetComponent<TestAcademy>();
-            var brain = TestBrain.Instantiate();
-            brain.brainParameters = new BrainParameters();
-            brain.brainParameters.vectorObservationSize = 0;
-            agent1.GiveBrain(brain);
-            agent2.GiveBrain(brain);
+            aca.resetParameters = new ResetParameters();
 
             Assert.AreEqual(false, agent1.IsDone());
             Assert.AreEqual(false, agent2.IsDone());
             Assert.AreEqual(1, agent2.initializeAgentCalls);
             Assert.AreEqual(0, agent1.agentActionCalls);
             Assert.AreEqual(0, agent2.agentActionCalls);
+
+            // Make sure the sensors were sorted
+            Assert.AreEqual(agent1.m_Sensors[0].GetName(), "testsensor1");
+            Assert.AreEqual(agent1.m_Sensors[1].GetName(), "testsensor2");
         }
     }
 
             var acaGo = new GameObject("TestAcademy");
             acaGo.AddComponent<TestAcademy>();
             var aca = acaGo.GetComponent<TestAcademy>();
+            aca.resetParameters = new ResetParameters();
             var academyInitializeMethod = typeof(Academy).GetMethod("InitializeEnvironment",
                 BindingFlags.Instance | BindingFlags.NonPublic);
             academyInitializeMethod?.Invoke(aca, new object[] { });
             var acaGo = new GameObject("TestAcademy");
             acaGo.AddComponent<TestAcademy>();
             var aca = acaGo.GetComponent<TestAcademy>();
-            var brain = TestBrain.Instantiate();
+            aca.resetParameters = new ResetParameters();
 
 
             var agentEnableMethod = typeof(Agent).GetMethod(
 
             agent1.agentParameters = new AgentParameters();
             agent2.agentParameters = new AgentParameters();
-            brain.brainParameters = new BrainParameters();
             // We use event based so the agent will now try to send anything to the brain
             agent1.agentParameters.onDemandDecision = false;
             agent1.agentParameters.numberOfActionsBetweenDecisions = 2;
-            brain.brainParameters.vectorObservationSize = 0;
-            brain.brainParameters.cameraResolutions = new Resolution[0];
-            agent1.GiveBrain(brain);
-            agent2.GiveBrain(brain);
 
             agentEnableMethod?.Invoke(agent1, new object[] { aca });
             academyInitializeMethod?.Invoke(aca, new object[] { });
             var acaGo = new GameObject("TestAcademy");
             acaGo.AddComponent<TestAcademy>();
             var aca = acaGo.GetComponent<TestAcademy>();
+            aca.resetParameters = new ResetParameters();
             var academyInitializeMethod = typeof(Academy).GetMethod(
                 "InitializeEnvironment", BindingFlags.Instance | BindingFlags.NonPublic);
             academyInitializeMethod?.Invoke(aca, new object[] { });
                 }
 
                 stepsSinceReset += 1;
-                academyStepMethod.Invoke((object)aca, new object[] { });
+                academyStepMethod.Invoke(aca, new object[] { });
             }
         }
 
             var acaGo = new GameObject("TestAcademy");
             acaGo.AddComponent<TestAcademy>();
             var aca = acaGo.GetComponent<TestAcademy>();
-            var brain = TestBrain.Instantiate();
+            aca.resetParameters = new ResetParameters();
 
 
             var agentEnableMethod = typeof(Agent).GetMethod(
 
             agent1.agentParameters = new AgentParameters();
             agent2.agentParameters = new AgentParameters();
-            brain.brainParameters = new BrainParameters();
-            // agent2 will request decisions only when RequestDecision is called
-            brain.brainParameters.vectorObservationSize = 0;
-            brain.brainParameters.cameraResolutions = new Resolution[0];
-            agent1.GiveBrain(brain);
-            agent2.GiveBrain(brain);
 
             agentEnableMethod?.Invoke(agent2, new object[] { aca });
             academyInitializeMethod?.Invoke(aca, new object[] { });
             var acaGo = new GameObject("TestAcademy");
             acaGo.AddComponent<TestAcademy>();
             var aca = acaGo.GetComponent<TestAcademy>();
-            var brain = TestBrain.Instantiate();
+            aca.resetParameters = new ResetParameters();
 
 
             var agentEnableMethod = typeof(Agent).GetMethod(
 
             agent1.agentParameters = new AgentParameters();
             agent2.agentParameters = new AgentParameters();
-            brain.brainParameters = new BrainParameters();
             // We use event based so the agent will now try to send anything to the brain
             agent1.agentParameters.onDemandDecision = false;
             // agent1 will take an action at every step and request a decision every steps
             //Here we specify that the agent does not reset when done
             agent1.agentParameters.resetOnDone = false;
             agent2.agentParameters.resetOnDone = false;
-            brain.brainParameters.vectorObservationSize = 0;
-            brain.brainParameters.cameraResolutions = new Resolution[0];
-            agent1.GiveBrain(brain);
-            agent2.GiveBrain(brain);
 
             agentEnableMethod?.Invoke(agent2, new object[] { aca });
             academyInitializeMethod?.Invoke(aca, new object[] { });
             var acaGo = new GameObject("TestAcademy");
             acaGo.AddComponent<TestAcademy>();
             var aca = acaGo.GetComponent<TestAcademy>();
-            var brain = TestBrain.Instantiate();
+            aca.resetParameters = new ResetParameters();
 
 
             var agentEnableMethod = typeof(Agent).GetMethod(
 
             agent1.agentParameters = new AgentParameters();
             agent2.agentParameters = new AgentParameters();
-            brain.brainParameters = new BrainParameters();
             // We use event based so the agent will now try to send anything to the brain
             agent1.agentParameters.onDemandDecision = false;
             agent1.agentParameters.numberOfActionsBetweenDecisions = 3;
             agent1.agentParameters.maxStep = 20;
-            brain.brainParameters.vectorObservationSize = 0;
-            brain.brainParameters.cameraResolutions = new Resolution[0];
-            agent1.GiveBrain(brain);
-            agent2.GiveBrain(brain);
 
             agentEnableMethod?.Invoke(agent2, new object[] { aca });
             academyInitializeMethod?.Invoke(aca, new object[] { });

UnitySDK/Assets/ML-Agents/Examples/3DBall/Prefabs/3DBall.prefab

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UnitySDK/Assets/ML-Agents/Examples/3DBall/Prefabs/3DBallHardNew.prefab

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UnitySDK/Assets/ML-Agents/Examples/3DBall/Scenes/3DBall.unity

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UnitySDK/Assets/ML-Agents/Examples/3DBall/Scenes/3DBallHard.unity

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UnitySDK/Assets/ML-Agents/Examples/3DBall/Scripts/Ball3DAgent.cs

 
     public override void AgentAction(float[] vectorAction, string textAction)
     {
-        if (brain.brainParameters.vectorActionSpaceType == SpaceType.Continuous)
+        var actionZ = 2f * Mathf.Clamp(vectorAction[0], -1f, 1f);
+        var actionX = 2f * Mathf.Clamp(vectorAction[1], -1f, 1f);
+
+        if ((gameObject.transform.rotation.z < 0.25f && actionZ > 0f) ||
+            (gameObject.transform.rotation.z > -0.25f && actionZ < 0f))
-            var actionZ = 2f * Mathf.Clamp(vectorAction[0], -1f, 1f);
-            var actionX = 2f * Mathf.Clamp(vectorAction[1], -1f, 1f);
+            gameObject.transform.Rotate(new Vector3(0, 0, 1), actionZ);
+        }
-            if ((gameObject.transform.rotation.z < 0.25f && actionZ > 0f) ||
-                (gameObject.transform.rotation.z > -0.25f && actionZ < 0f))
-            {
-                gameObject.transform.Rotate(new Vector3(0, 0, 1), actionZ);
-            }
-
-            if ((gameObject.transform.rotation.x < 0.25f && actionX > 0f) ||
-                (gameObject.transform.rotation.x > -0.25f && actionX < 0f))
-            {
-                gameObject.transform.Rotate(new Vector3(1, 0, 0), actionX);
-            }
+        if ((gameObject.transform.rotation.x < 0.25f && actionX > 0f) ||
+            (gameObject.transform.rotation.x > -0.25f && actionX < 0f))
+        {
+            gameObject.transform.Rotate(new Vector3(1, 0, 0), actionX);
         }
         if ((ball.transform.position.y - gameObject.transform.position.y) < -2f ||
             Mathf.Abs(ball.transform.position.x - gameObject.transform.position.x) > 3f ||
             + gameObject.transform.position;
         //Reset the parameters when the Agent is reset.
         SetResetParameters();
+    }
+
+    public override float[] Heuristic()
+    {
+        var action = new float[2];
+
+        action[0] = -Input.GetAxis("Horizontal");
+        action[1] = Input.GetAxis("Vertical");
+        return action;
     }
 
     public void SetBall()

UnitySDK/Assets/ML-Agents/Examples/3DBall/Scripts/Ball3DHardAgent.cs

 
     public override void AgentAction(float[] vectorAction, string textAction)
     {
-        if (brain.brainParameters.vectorActionSpaceType == SpaceType.Continuous)
+        var actionZ = 2f * Mathf.Clamp(vectorAction[0], -1f, 1f);
+        var actionX = 2f * Mathf.Clamp(vectorAction[1], -1f, 1f);
+
+        if ((gameObject.transform.rotation.z < 0.25f && actionZ > 0f) ||
+            (gameObject.transform.rotation.z > -0.25f && actionZ < 0f))
-            var actionZ = 2f * Mathf.Clamp(vectorAction[0], -1f, 1f);
-            var actionX = 2f * Mathf.Clamp(vectorAction[1], -1f, 1f);
+            gameObject.transform.Rotate(new Vector3(0, 0, 1), actionZ);
+        }
-            if ((gameObject.transform.rotation.z < 0.25f && actionZ > 0f) ||
-                (gameObject.transform.rotation.z > -0.25f && actionZ < 0f))
-            {
-                gameObject.transform.Rotate(new Vector3(0, 0, 1), actionZ);
-            }
-
-            if ((gameObject.transform.rotation.x < 0.25f && actionX > 0f) ||
-                (gameObject.transform.rotation.x > -0.25f && actionX < 0f))
-            {
-                gameObject.transform.Rotate(new Vector3(1, 0, 0), actionX);
-            }
+        if ((gameObject.transform.rotation.x < 0.25f && actionX > 0f) ||
+            (gameObject.transform.rotation.x > -0.25f && actionX < 0f))
+        {
+            gameObject.transform.Rotate(new Vector3(1, 0, 0), actionX);
         }
         if ((ball.transform.position.y - gameObject.transform.position.y) < -2f ||
             Mathf.Abs(ball.transform.position.x - gameObject.transform.position.x) > 3f ||

UnitySDK/Assets/ML-Agents/Examples/3DBall/TFModels/3DBallHard.nn

-vector_observation����

-epsilon����

actionaction_probsaction_output_shape������?action_output_shape




memory_size





version_number




is_continuous_control




running_variance


--running_mean


-1-clip_by_value/y



^
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