Merge branch 'master' into develop-centralizedcritic-mm

.github/workflows/pytest.yml

     strategy:
       matrix:
         python-version: [3.6.x, 3.7.x, 3.8.x]
-        include:
-          - python-version: 3.6.x
-            pip_constraints: test_constraints_min_version.txt
-          - python-version: 3.7.x
-            pip_constraints: test_constraints_max_tf1_version.txt
-          - python-version: 3.8.x
-            pip_constraints: test_constraints_max_tf2_version.txt
     steps:
     - uses: actions/checkout@v2
     - name: Set up Python
         # This path is specific to Ubuntu
         path: ~/.cache/pip
         # Look to see if there is a cache hit for the corresponding requirements file
-        key: ${{ runner.os }}-pip-${{ hashFiles('ml-agents/setup.py', 'ml-agents-envs/setup.py', 'gym-unity/setup.py', 'test_requirements.txt', matrix.pip_constraints) }}
+        key: ${{ runner.os }}-pip-${{ hashFiles('ml-agents/setup.py', 'ml-agents-envs/setup.py', 'gym-unity/setup.py', 'test_requirements.txt') }}
         restore-keys: |
           ${{ runner.os }}-pip-
           ${{ runner.os }}-
         # pin pip to workaround https://github.com/pypa/pip/issues/9180
         python -m pip install pip==20.2
         python -m pip install --upgrade setuptools
-        python -m pip install --progress-bar=off -e ./ml-agents-envs -c ${{ matrix.pip_constraints }}
-        python -m pip install --progress-bar=off -e ./ml-agents -c ${{ matrix.pip_constraints }}
-        python -m pip install --progress-bar=off -r test_requirements.txt -c ${{ matrix.pip_constraints }}
-        python -m pip install --progress-bar=off -e ./gym-unity -c ${{ matrix.pip_constraints }}
+        python -m pip install --progress-bar=off -e ./ml-agents-envs
+        python -m pip install --progress-bar=off -e ./ml-agents
+        python -m pip install --progress-bar=off -r test_requirements.txt
+        python -m pip install --progress-bar=off -e ./gym-unity
     - name: Save python dependencies
       run: |
         pip freeze > pip_versions-${{ matrix.python-version }}.txt

.yamato/com.unity.ml-agents-test.yml

     enableCodeCoverage: !!bool false
     # We want some scene tests to run in the DevProject, but packages there only support 2019+
     testProject: Project
+    enableNoDefaultPackages: !!bool false
+    enableNoDefaultPackages: !!bool true
+    enableNoDefaultPackages: !!bool true
+    enableNoDefaultPackages: !!bool true
 
 trunk_editor:
   - version: trunk
 {% else %}
   {% assign coverageOptions = "" %}
 {% endif %}
+
+{% if editor.enableNoDefaultPackages %}
+  {% assign noDefaultPackagesOptions = "--extra-create-project-arg='-upmNoDefaultPackages'" %}
+{% else %}
+  {% assign noDefaultPackagesOptions = "" %}
+{% endif %}
+
 test_{{ package.name }}_{{ platform.name }}_{{ editor.version }}:
   name : {{ package.name }} test {{ editor.version }} on {{ platform.name }}
   agent:
   commands:
     - npm install upm-ci-utils@stable -g --registry https://artifactory.prd.cds.internal.unity3d.com/artifactory/api/npm/upm-npm
-    - upm-ci project test -u {{ editor.version }} --project-path {{ editor.testProject }} --package-filter {{ package.name }} {{ coverageOptions }} --extra-utr-arg "reruncount=2"
+    - upm-ci project test -u {{ editor.version }} --project-path {{ editor.testProject }} --package-filter {{ package.name }} {{ coverageOptions }} {{ noDefaultPackagesOptions }} --extra-utr-arg "reruncount=2"
     {% if editor.enableCodeCoverage %}
     - python3 ml-agents/tests/yamato/check_coverage_percent.py upm-ci~/test-results/ {{ package.minCoveragePct }}
     {% endif %}
     - python -m pip install unity-downloader-cli --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple --upgrade
     - unity-downloader-cli -u trunk -c editor --wait --fast
     - npm install upm-ci-utils@stable -g --registry https://artifactory.prd.cds.internal.unity3d.com/artifactory/api/npm/upm-npm
-    - upm-ci project test -u {{ editor.version }} --project-path {{ editor.testProject }} --package-filter {{ package.name }} {{ coverageOptions }} --extra-utr-arg "reruncount=2"
+    - upm-ci project test -u {{ editor.version }} --project-path {{ editor.testProject }} --package-filter {{ package.name }} {{ coverageOptions }} --extra-create-project-arg="-upmNoDefaultPackages" --extra-utr-arg "reruncount=2"
     {% if editor.enableCodeCoverage %}
     - python3 ml-agents/tests/yamato/check_coverage_percent.py upm-ci~/test-results/ {{ package.minCoveragePct }}
     {% endif %}

.yamato/protobuf-generation-test.yml

   name: Protobuf Generation Tests
   agent:
     type: Unity::VM::osx
-    image: ml-agents/ml-agents-bokken-mac:0.1.4-492264
+    image: package-ci/mac:stable
+    HOMEBREW_NO_AUTO_UPDATE: "1"
   commands:
     - |
       brew install nuget

DevProject/Packages/manifest.json

   "dependencies": {
     "com.unity.2d.sprite": "1.0.0",
     "com.unity.2d.tilemap": "1.0.0",
-    "com.unity.ads": "3.4.7",
+    "com.unity.ads": "3.4.9",
     "com.unity.analytics": "3.3.5",
     "com.unity.coding": "0.1.0-preview.13",
     "com.unity.collab-proxy": "1.2.16",
     "com.unity.ml-agents.extensions": "file:../../com.unity.ml-agents.extensions",
     "com.unity.multiplayer-hlapi": "1.0.6",
-    "com.unity.package-manager-doctools": "1.1.1-preview.3",
-    "com.unity.package-validation-suite": "0.14.0-preview",
-    "com.unity.purchasing": "2.0.6",
-    "com.unity.test-framework": "1.1.14",
+    "com.unity.package-manager-doctools": "1.7.0-preview",
+    "com.unity.package-validation-suite": "0.19.0-preview",
+    "com.unity.purchasing": "2.1.0",
+    "com.unity.test-framework": "1.1.16",
     "com.unity.test-framework.performance": "2.2.0-preview",
     "com.unity.testtools.codecoverage": "0.2.2-preview",
     "com.unity.textmeshpro": "2.0.1",

Dockerfile

-FROM nvidia/cuda:10.0-cudnn7-devel-ubuntu18.04
+# From https://gitlab.com/nvidia/container-images/cuda/blob/master/doc/supported-tags.md
+FROM nvidia/cuda:10.2-cudnn7-devel-ubuntu18.04
 
 RUN yes | unminimize
 
   apt-get install -y --no-install-recommends wget curl tmux vim git gdebi-core \
   build-essential python3-pip unzip google-cloud-sdk htop mesa-utils xorg-dev xorg \
-  libglvnd-dev libgl1-mesa-dev libegl1-mesa-dev libgles2-mesa-dev && \
+  libglvnd-dev libgl1-mesa-dev libegl1-mesa-dev libgles2-mesa-dev xvfb && \
-  wget http://security.ubuntu.com/ubuntu/pool/universe/x/xorg-server/xvfb_1.18.4-0ubuntu0.10_amd64.deb && \
-  yes | gdebi libxfont1_1.5.1-1ubuntu0.16.04.4_amd64.deb && \
-  yes | gdebi xvfb_1.18.4-0ubuntu0.10_amd64.deb
+  yes | gdebi libxfont1_1.5.1-1ubuntu0.16.04.4_amd64.deb
 RUN python3 -m pip install --upgrade pip
 RUN pip install setuptools==41.0.0
 

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

-using System;
 using UnityEngine;
 using Unity.MLAgents;
 using Unity.MLAgents.Actuators;

Project/Assets/ML-Agents/Examples/Basic/Scripts/BasicController.cs

 using UnityEngine;
 using UnityEngine.SceneManagement;
 using Unity.MLAgents;
-using Unity.MLAgents.Actuators;
 using UnityEngine.Serialization;
 
 /// <summary>

Project/Assets/ML-Agents/Examples/Crawler/Scripts/CrawlerAgent.cs

-using System;
 using UnityEngine;
 using Unity.MLAgents;
 using Unity.Barracuda;
         Vector3 avgVel = Vector3.zero;
 
         //ALL RBS
-        int numOfRB = 0;
+        int numOfRb = 0;
-            numOfRB++;
+            numOfRb++;
-        avgVel = velSum / numOfRB;
+        avgVel = velSum / numOfRb;
         return avgVel;
     }
 

Project/Assets/ML-Agents/Examples/FoodCollector/Prefabs/FoodCollectorArea.prefab

   m_BrainParameters:
     VectorObservationSize: 4
     NumStackedVectorObservations: 1
-    VectorActionSize: 03000000030000000300000002000000
+    m_ActionSpec:
+      m_NumContinuousActions: 3
+      BranchSizes: 02000000
+    VectorActionSize: 
-    VectorActionSpaceType: 0
-  m_Model: {fileID: 11400000, guid: 36ab3e93020504f48858d0856f939685, type: 3}
+    VectorActionSpaceType: 1
+    hasUpgradedBrainParametersWithActionSpec: 1
+  m_Model: {fileID: 11400000, guid: 3210b528a2bc44a86bd6bd1d571070f8, type: 3}
+  m_UseChildActuators: 1
   m_ObservableAttributeHandling: 0
 --- !u!114 &114176228333253036
 MonoBehaviour:
   myLaser: {fileID: 1081721624670010}
   contribute: 1
   useVectorObs: 1
+  useVectorFrozenFlag: 0
 --- !u!114 &114725457980523372
 MonoBehaviour:
   m_ObjectHideFlags: 0
   m_BrainParameters:
     VectorObservationSize: 4
     NumStackedVectorObservations: 1
-    VectorActionSize: 03000000030000000300000002000000
+    m_ActionSpec:
+      m_NumContinuousActions: 3
+      BranchSizes: 02000000
+    VectorActionSize: 
-    VectorActionSpaceType: 0
-  m_Model: {fileID: 11400000, guid: 36ab3e93020504f48858d0856f939685, type: 3}
+    VectorActionSpaceType: 1
+    hasUpgradedBrainParametersWithActionSpec: 1
+  m_Model: {fileID: 11400000, guid: 3210b528a2bc44a86bd6bd1d571070f8, type: 3}
+  m_UseChildActuators: 1
   m_ObservableAttributeHandling: 0
 --- !u!114 &114711827726849508
 MonoBehaviour:
   myLaser: {fileID: 1941433838307300}
   contribute: 0
   useVectorObs: 1
+  useVectorFrozenFlag: 0
 --- !u!114 &114443152683847924
 MonoBehaviour:
   m_ObjectHideFlags: 0
   m_BrainParameters:
     VectorObservationSize: 4
     NumStackedVectorObservations: 1
-    VectorActionSize: 03000000030000000300000002000000
+    m_ActionSpec:
+      m_NumContinuousActions: 3
+      BranchSizes: 02000000
+    VectorActionSize: 
-    VectorActionSpaceType: 0
-  m_Model: {fileID: 11400000, guid: 36ab3e93020504f48858d0856f939685, type: 3}
+    VectorActionSpaceType: 1
+    hasUpgradedBrainParametersWithActionSpec: 1
+  m_Model: {fileID: 11400000, guid: 3210b528a2bc44a86bd6bd1d571070f8, type: 3}
+  m_UseChildActuators: 1
   m_ObservableAttributeHandling: 0
 --- !u!114 &114542632553128056
 MonoBehaviour:
   myLaser: {fileID: 1421240237750412}
   contribute: 0
   useVectorObs: 1
+  useVectorFrozenFlag: 0
 --- !u!114 &114986980423924774
 MonoBehaviour:
   m_ObjectHideFlags: 0
   m_BrainParameters:
     VectorObservationSize: 4
     NumStackedVectorObservations: 1
-    VectorActionSize: 03000000030000000300000002000000
+    m_ActionSpec:
+      m_NumContinuousActions: 3
+      BranchSizes: 02000000
+    VectorActionSize: 
-    VectorActionSpaceType: 0
-  m_Model: {fileID: 11400000, guid: 36ab3e93020504f48858d0856f939685, type: 3}
+    VectorActionSpaceType: 1
+    hasUpgradedBrainParametersWithActionSpec: 1
+  m_Model: {fileID: 11400000, guid: 3210b528a2bc44a86bd6bd1d571070f8, type: 3}
+  m_UseChildActuators: 1
   m_ObservableAttributeHandling: 0
 --- !u!114 &114189751434580810
 MonoBehaviour:
   myLaser: {fileID: 1617924810425504}
   contribute: 0
   useVectorObs: 1
+  useVectorFrozenFlag: 0
 --- !u!114 &114644889237473510
 MonoBehaviour:
   m_ObjectHideFlags: 0
   m_BrainParameters:
     VectorObservationSize: 4
     NumStackedVectorObservations: 1
-    VectorActionSize: 03000000030000000300000002000000
+    m_ActionSpec:
+      m_NumContinuousActions: 3
+      BranchSizes: 02000000
+    VectorActionSize: 
-    VectorActionSpaceType: 0
-  m_Model: {fileID: 11400000, guid: 36ab3e93020504f48858d0856f939685, type: 3}
+    VectorActionSpaceType: 1
+    hasUpgradedBrainParametersWithActionSpec: 1
+  m_Model: {fileID: 11400000, guid: 3210b528a2bc44a86bd6bd1d571070f8, type: 3}
+  m_UseChildActuators: 1
   m_ObservableAttributeHandling: 0
 --- !u!114 &114235147148547996
 MonoBehaviour:
   myLaser: {fileID: 1045923826166930}
   contribute: 0
   useVectorObs: 1
+  useVectorFrozenFlag: 0
 --- !u!114 &114276061479012222
 MonoBehaviour:
   m_ObjectHideFlags: 0
   m_PrefabInstance: {fileID: 0}
   m_PrefabAsset: {fileID: 0}
   m_GameObject: {fileID: 1819751139121548}
-  m_LocalRotation: {x: -0, y: -0, z: -0, w: 1}
+  m_LocalRotation: {x: 0, y: 0, z: 0, w: 1}
   m_LocalPosition: {x: 0, y: 12.3, z: 0}
   m_LocalScale: {x: 1, y: 1, z: 1}
   m_Children:

Project/Assets/ML-Agents/Examples/FoodCollector/Prefabs/GridFoodCollectorArea.prefab

   m_BrainParameters:
     VectorObservationSize: 0
     NumStackedVectorObservations: 1
-    VectorActionSize: 03000000030000000300000002000000
+    m_ActionSpec:
+      m_NumContinuousActions: 3
+      BranchSizes: 02000000
+    VectorActionSize: 
-  m_Model: {fileID: 11400000, guid: 699f852e79b5ba642871514fb1fb9843, type: 3}
+    hasUpgradedBrainParametersWithActionSpec: 1
+  m_Model: {fileID: 11400000, guid: 75910f45f20be49b18e2b95879a217b2, type: 3}
   m_InferenceDevice: 0
   m_BehaviorType: 0
   m_BehaviorName: GridFoodCollector
   myLaser: {fileID: 1081721624670010}
   contribute: 0
   useVectorObs: 0
+  useVectorFrozenFlag: 0
 --- !u!114 &8297075921230369060
 MonoBehaviour:
   m_ObjectHideFlags: 0
   - {r: 0, g: 0, b: 0, a: 0}
   GizmoYOffset: 0
   ShowGizmos: 0
+  CompressionType: 1
 --- !u!1 &1482701732800114
 GameObject:
   m_ObjectHideFlags: 0
   m_BrainParameters:
     VectorObservationSize: 0
     NumStackedVectorObservations: 1
-    VectorActionSize: 03000000030000000300000002000000
+    m_ActionSpec:
+      m_NumContinuousActions: 3
+      BranchSizes: 02000000
+    VectorActionSize: 
-  m_Model: {fileID: 11400000, guid: 699f852e79b5ba642871514fb1fb9843, type: 3}
+    hasUpgradedBrainParametersWithActionSpec: 1
+  m_Model: {fileID: 11400000, guid: 75910f45f20be49b18e2b95879a217b2, type: 3}
   m_InferenceDevice: 0
   m_BehaviorType: 0
   m_BehaviorName: GridFoodCollector
   myLaser: {fileID: 1941433838307300}
   contribute: 0
   useVectorObs: 0
+  useVectorFrozenFlag: 0
 --- !u!114 &259154752087955944
 MonoBehaviour:
   m_ObjectHideFlags: 0
   - {r: 0, g: 0, b: 0, a: 0}
   GizmoYOffset: 0
   ShowGizmos: 0
+  CompressionType: 1
 --- !u!1 &1528397385587768
 GameObject:
   m_ObjectHideFlags: 0
   m_BrainParameters:
     VectorObservationSize: 0
     NumStackedVectorObservations: 1
-    VectorActionSize: 03000000030000000300000002000000
+    m_ActionSpec:
+      m_NumContinuousActions: 3
+      BranchSizes: 02000000
+    VectorActionSize: 
-  m_Model: {fileID: 11400000, guid: 699f852e79b5ba642871514fb1fb9843, type: 3}
+    hasUpgradedBrainParametersWithActionSpec: 1
+  m_Model: {fileID: 11400000, guid: 75910f45f20be49b18e2b95879a217b2, type: 3}
   m_InferenceDevice: 0
   m_BehaviorType: 0
   m_BehaviorName: GridFoodCollector
   myLaser: {fileID: 1421240237750412}
   contribute: 0
   useVectorObs: 0
+  useVectorFrozenFlag: 0
 --- !u!114 &5519119940433428255
 MonoBehaviour:
   m_ObjectHideFlags: 0
   - {r: 0, g: 0, b: 0, a: 0}
   GizmoYOffset: 0
   ShowGizmos: 0
+  CompressionType: 1
 --- !u!1 &1617924810425504
 GameObject:
   m_ObjectHideFlags: 0
   m_BrainParameters:
     VectorObservationSize: 0
     NumStackedVectorObservations: 1
-    VectorActionSize: 03000000030000000300000002000000
+    m_ActionSpec:
+      m_NumContinuousActions: 3
+      BranchSizes: 02000000
+    VectorActionSize: 
-  m_Model: {fileID: 11400000, guid: 699f852e79b5ba642871514fb1fb9843, type: 3}
+    hasUpgradedBrainParametersWithActionSpec: 1
+  m_Model: {fileID: 11400000, guid: 75910f45f20be49b18e2b95879a217b2, type: 3}
   m_InferenceDevice: 0
   m_BehaviorType: 0
   m_BehaviorName: GridFoodCollector
   myLaser: {fileID: 1617924810425504}
   contribute: 0
   useVectorObs: 0
+  useVectorFrozenFlag: 0
 --- !u!114 &5884750436653390196
 MonoBehaviour:
   m_ObjectHideFlags: 0
   - {r: 0, g: 0, b: 0, a: 0}
   GizmoYOffset: 0
   ShowGizmos: 0
+  CompressionType: 1
 --- !u!1 &1688105343773098
 GameObject:
   m_ObjectHideFlags: 0
   m_BrainParameters:
     VectorObservationSize: 0
     NumStackedVectorObservations: 1
-    VectorActionSize: 03000000030000000300000002000000
+    m_ActionSpec:
+      m_NumContinuousActions: 3
+      BranchSizes: 02000000
+    VectorActionSize: 
-  m_Model: {fileID: 11400000, guid: 699f852e79b5ba642871514fb1fb9843, type: 3}
+    hasUpgradedBrainParametersWithActionSpec: 1
+  m_Model: {fileID: 11400000, guid: 75910f45f20be49b18e2b95879a217b2, type: 3}
   m_InferenceDevice: 0
   m_BehaviorType: 0
   m_BehaviorName: GridFoodCollector
   myLaser: {fileID: 1045923826166930}
   contribute: 0
   useVectorObs: 0
+  useVectorFrozenFlag: 0
 --- !u!114 &4768752321433982785
 MonoBehaviour:
   m_ObjectHideFlags: 0
   - {r: 0, g: 0, b: 0, a: 0}
   GizmoYOffset: 0
   ShowGizmos: 0
+  CompressionType: 1
 --- !u!1 &1729825611722018
 GameObject:
   m_ObjectHideFlags: 0
   m_PrefabInstance: {fileID: 0}
   m_PrefabAsset: {fileID: 0}
   m_GameObject: {fileID: 1819751139121548}
-  m_LocalRotation: {x: -0, y: -0, z: -0, w: 1}
+  m_LocalRotation: {x: 0, y: 0, z: 0, w: 1}
   m_LocalPosition: {x: 0, y: 12.3, z: 0}
   m_LocalScale: {x: 1, y: 1, z: 1}
   m_Children:

Project/Assets/ML-Agents/Examples/FoodCollector/Prefabs/VisualFoodCollectorArea.prefab

   m_BrainParameters:
     VectorObservationSize: 1
     NumStackedVectorObservations: 1
-    VectorActionSize: 03000000030000000300000002000000
+    m_ActionSpec:
+      m_NumContinuousActions: 3
+      BranchSizes: 02000000
+    VectorActionSize: 
-  m_Model: {fileID: 11400000, guid: c3b1eb0bcf06b4c0488599c7ab806de7, type: 3}
+    hasUpgradedBrainParametersWithActionSpec: 1
+  m_Model: {fileID: 11400000, guid: ec4b31b5d66ca4e51ae3ac41945facb2, type: 3}
   m_InferenceDevice: 0
   m_BehaviorType: 0
   m_BehaviorName: VisualFoodCollector
   m_BrainParameters:
     VectorObservationSize: 1
     NumStackedVectorObservations: 1
-    VectorActionSize: 03000000030000000300000002000000
+    m_ActionSpec:
+      m_NumContinuousActions: 3
+      BranchSizes: 02000000
+    VectorActionSize: 
-  m_Model: {fileID: 11400000, guid: c3b1eb0bcf06b4c0488599c7ab806de7, type: 3}
+    hasUpgradedBrainParametersWithActionSpec: 1
+  m_Model: {fileID: 11400000, guid: ec4b31b5d66ca4e51ae3ac41945facb2, type: 3}
   m_InferenceDevice: 0
   m_BehaviorType: 0
   m_BehaviorName: VisualFoodCollector
   m_BrainParameters:
     VectorObservationSize: 1
     NumStackedVectorObservations: 1
-    VectorActionSize: 03000000030000000300000002000000
+    m_ActionSpec:
+      m_NumContinuousActions: 3
+      BranchSizes: 02000000
+    VectorActionSize: 
-  m_Model: {fileID: 11400000, guid: c3b1eb0bcf06b4c0488599c7ab806de7, type: 3}
+    hasUpgradedBrainParametersWithActionSpec: 1
+  m_Model: {fileID: 11400000, guid: ec4b31b5d66ca4e51ae3ac41945facb2, type: 3}
   m_InferenceDevice: 0
   m_BehaviorType: 0
   m_BehaviorName: VisualFoodCollector
   m_BrainParameters:
     VectorObservationSize: 1
     NumStackedVectorObservations: 1
-    VectorActionSize: 03000000030000000300000002000000
+    m_ActionSpec:
+      m_NumContinuousActions: 3
+      BranchSizes: 02000000
+    VectorActionSize: 
-  m_Model: {fileID: 11400000, guid: c3b1eb0bcf06b4c0488599c7ab806de7, type: 3}
+    hasUpgradedBrainParametersWithActionSpec: 1
+  m_Model: {fileID: 11400000, guid: ec4b31b5d66ca4e51ae3ac41945facb2, type: 3}
   m_InferenceDevice: 0
   m_BehaviorType: 0
   m_BehaviorName: VisualFoodCollector

Project/Assets/ML-Agents/Examples/FoodCollector/Scripts/FoodCollectorAgent.cs

-using System;
 using UnityEngine;
 using Unity.MLAgents;
 using Unity.MLAgents.Actuators;
         return new Color32(r, g, b, 255);
     }
 
-    public void MoveAgent(ActionSegment<int> act)
+    public void MoveAgent(ActionBuffers actionBuffers)
     {
         m_Shoot = false;
 
         var dirToGo = Vector3.zero;
         var rotateDir = Vector3.zero;
 
+        var continuousActions = actionBuffers.ContinuousActions;
+        var discreteActions = actionBuffers.DiscreteActions;
+
-            var shootCommand = false;
-            var forwardAxis = (int)act[0];
-            var rightAxis = (int)act[1];
-            var rotateAxis = (int)act[2];
-            var shootAxis = (int)act[3];
-
-            switch (forwardAxis)
-            {
-                case 1:
-                    dirToGo = transform.forward;
-                    break;
-                case 2:
-                    dirToGo = -transform.forward;
-                    break;
-            }
+            var forward = Mathf.Clamp(continuousActions[0], -1f, 1f);
+            var right = Mathf.Clamp(continuousActions[1], -1f, 1f);
+            var rotate = Mathf.Clamp(continuousActions[2], -1f, 1f);
-            switch (rightAxis)
-            {
-                case 1:
-                    dirToGo = transform.right;
-                    break;
-                case 2:
-                    dirToGo = -transform.right;
-                    break;
-            }
+            dirToGo = transform.forward * forward;
+            dirToGo += transform.right * right;
+            rotateDir = -transform.up * rotate;
-            switch (rotateAxis)
-            {
-                case 1:
-                    rotateDir = -transform.up;
-                    break;
-                case 2:
-                    rotateDir = transform.up;
-                    break;
-            }
-            switch (shootAxis)
-            {
-                case 1:
-                    shootCommand = true;
-                    break;
-            }
+            var shootCommand = (int)discreteActions[0] > 0;
             if (shootCommand)
             {
                 m_Shoot = true;
     public override void OnActionReceived(ActionBuffers actionBuffers)
 
     {
-        MoveAgent(actionBuffers.DiscreteActions);
+        MoveAgent(actionBuffers);
-        var discreteActionsOut = actionsOut.DiscreteActions;
-        discreteActionsOut[0] = 0;
-        discreteActionsOut[1] = 0;
-        discreteActionsOut[2] = 0;
+        var continuousActionsOut = actionsOut.ContinuousActions;
+        continuousActionsOut[0] = 0;
+        continuousActionsOut[1] = 0;
+        continuousActionsOut[2] = 0;
-            discreteActionsOut[2] = 2;
+            continuousActionsOut[2] = 1;
-            discreteActionsOut[0] = 1;
+            continuousActionsOut[0] = 1;
-            discreteActionsOut[2] = 1;
+            continuousActionsOut[2] = -1;
-            discreteActionsOut[0] = 2;
+            continuousActionsOut[0] = -1;
-        discreteActionsOut[3] = Input.GetKey(KeyCode.Space) ? 1 : 0;
+        var discreteActionsOut = actionsOut.DiscreteActions;
+        discreteActionsOut[0] = Input.GetKey(KeyCode.Space) ? 1 : 0;
     }
 
     public override void OnEpisodeBegin()

Project/Assets/ML-Agents/Examples/GridWorld/Scenes/GridWorld.unity

   m_ReflectionIntensity: 1
   m_CustomReflection: {fileID: 0}
   m_Sun: {fileID: 0}
-  m_IndirectSpecularColor: {r: 0.44971168, g: 0.4997775, b: 0.57563686, a: 1}
+  m_IndirectSpecularColor: {r: 0.44971228, g: 0.49977815, b: 0.57563734, a: 1}
   m_UseRadianceAmbientProbe: 0
 --- !u!157 &3
 LightmapSettings:
   agentParameters:
     maxStep: 100
   hasUpgradedFromAgentParameters: 1
-  maxStep: 100
+  MaxStep: 100
   area: {fileID: 1795599557}
   timeBetweenDecisionsAtInference: 0.15
   renderCamera: {fileID: 797520692}
   m_Name: 
   m_EditorClassIdentifier: 
   m_BrainParameters:
-    vectorObservationSize: 0
-    numStackedVectorObservations: 1
-    vectorActionSize: 05000000
-    vectorActionDescriptions: []
-    vectorActionSpaceType: 0
+    VectorObservationSize: 0
+    NumStackedVectorObservations: 1
+    VectorActionSize: 05000000
+    VectorActionDescriptions: []
+    VectorActionSpaceType: 0
   m_Model: {fileID: 11400000, guid: a812f1ce7763a4a0c912717f3594fe20, type: 3}
   m_InferenceDevice: 0
   m_BehaviorType: 0
+  m_UseChildActuators: 1
+  m_ObservableAttributeHandling: 0
 --- !u!114 &125487791
 MonoBehaviour:
   m_ObjectHideFlags: 0
   m_RenderTexture: {fileID: 8400000, guid: 114608d5384404f89bff4b6f88432958, type: 2}
   m_SensorName: RenderTextureSensor
   m_Grayscale: 0
+  m_ObservationStacks: 1
   m_Compression: 1
 --- !u!1 &260425459
 GameObject:
   trueAgent: {fileID: 125487785}
   goalPref: {fileID: 1508142483324970, guid: 1ec4e4e96e7514d45b7ebc3ba5a9a481, type: 3}
   pitPref: {fileID: 1811317785436014, guid: d13ee2db77b3a4dcc8664d2fe2a0f219, type: 3}
+  numberOfObstacles: 1
 --- !u!4 &1795599558
 Transform:
   m_ObjectHideFlags: 0

Project/Assets/ML-Agents/Examples/GridWorld/Scripts/GridArea.cs

     public GameObject goalPref;
     public GameObject pitPref;
     GameObject[] m_Objects;
+    public int numberOfObstacles = 1;
 
     GameObject m_Plane;
     GameObject m_Sn;
         transform.position = m_InitialPosition * (m_ResetParams.GetWithDefault("gridSize", 5f) + 1);
         var playersList = new List<int>();
 
-        for (var i = 0; i < (int)m_ResetParams.GetWithDefault("numObstacles", 1); i++)
+        for (var i = 0; i < (int)m_ResetParams.GetWithDefault("numObstacles", numberOfObstacles); i++)
         {
             playersList.Add(1);
         }

Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3Agent.cs

 
             m_TimeUntilMove = MoveTime;
 
-            var nextState = State.Invalid;
+            State nextState;
             switch (m_CurrentState)
             {
                 case State.FindMatches:
 
         bool HasValidMoves()
         {
-            foreach (var move in Board.ValidMoves())
+            foreach (var unused in Board.ValidMoves())
             {
                 return true;
             }

Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3Drawer.cs

 using System.Collections.Generic;
 using UnityEngine;
-using Unity.MLAgents.Extensions.Match3;
 
 namespace Unity.MLAgentsExamples
 {

Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3TileSelector.cs

-using System.Collections;
 using System.Collections.Generic;
 using UnityEngine;
 
         {
             AllTilesOff();
             emptyTile.SetActive(true);
-            return;
         }
         else
         {

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

-using System;
 using UnityEngine;
 using Unity.MLAgents;
 using Unity.MLAgents.Actuators;

Project/Assets/ML-Agents/Examples/Template/Scripts/TemplateAgent.cs

-using UnityEngine;
 using Unity.MLAgents;
 using Unity.MLAgents.Actuators;
 using Unity.MLAgents.Sensors;

Project/Assets/ML-Agents/Examples/Walker/Scripts/WalkerAgent.cs

     const float m_maxWalkingSpeed = 10; //The max walking speed
 
     //Should the agent sample a new goal velocity each episode?
-    //If true, walkSpeed will be randomly set between zero and m_maxWalkingSpeed in OnEpisodeBegin() 
+    //If true, walkSpeed will be randomly set between zero and m_maxWalkingSpeed in OnEpisodeBegin()
     //If false, the goal velocity will be walkingSpeed
     public bool randomizeWalkSpeedEachEpisode;
 
         //ragdoll's avg vel
         var avgVel = GetAvgVelocity();
 
-        //current ragdoll velocity. normalized 
+        //current ragdoll velocity. normalized
         sensor.AddObservation(Vector3.Distance(velGoal, avgVel));
         //avg body vel relative to cube
         sensor.AddObservation(m_OrientationCube.transform.InverseTransformDirection(avgVel));
         Vector3 avgVel = Vector3.zero;
 
         //ALL RBS
-        int numOfRB = 0;
+        int numOfRb = 0;
-            numOfRB++;
+            numOfRb++;
-        avgVel = velSum / numOfRB;
+        avgVel = velSum / numOfRb;
         return avgVel;
     }
 

Project/Assets/ML-Agents/TestScenes/TestCompressedTexture/TestTextureSensor.cs

 using UnityEngine;
 using Unity.MLAgents.Sensors;
-using Unity.MLAgents;
 
 public class TestTextureSensor : ISensor
 {

Project/Assets/ML-Agents/TestScenes/TestCompressedTexture/TestTextureSensorComponent.cs

 using UnityEngine;
-using UnityEngine.Serialization;
 using Unity.MLAgents.Sensors;
 
 

README.md

 
 # Unity ML-Agents Toolkit
 
-[![docs badge](https://img.shields.io/badge/docs-reference-blue.svg)](https://github.com/Unity-Technologies/ml-agents/tree/release_10_docs/docs/)
+[![docs badge](https://img.shields.io/badge/docs-reference-blue.svg)](https://github.com/Unity-Technologies/ml-agents/tree/release_12_docs/docs/)
 
 [![license badge](https://img.shields.io/badge/license-Apache--2.0-green.svg)](LICENSE)
 
 
 ## Releases & Documentation
 
-**Our latest, stable release is `Release 10`. Click
-[here](https://github.com/Unity-Technologies/ml-agents/tree/release_10_docs/docs/Readme.md)
+**Our latest, stable release is `Release 12`. Click
+[here](https://github.com/Unity-Technologies/ml-agents/tree/release_12_docs/docs/Readme.md)
 to get started with the latest release of ML-Agents.**
 
 The table below lists all our releases, including our `master` branch which is
 | **Version** | **Release Date** | **Source** | **Documentation** | **Download** |
 |:-------:|:------:|:-------------:|:-------:|:------------:|
 | **master (unstable)** | -- | [source](https://github.com/Unity-Technologies/ml-agents/tree/master) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/master/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/master.zip) |
-| **Release 10** | **November 18, 2020** | **[source](https://github.com/Unity-Technologies/ml-agents/tree/release_10)** | **[docs](https://github.com/Unity-Technologies/ml-agents/tree/release_10_docs/docs/Readme.md)** | **[download](https://github.com/Unity-Technologies/ml-agents/archive/release_10.zip)** |
+| **Release 12** | **December 22, 2020** | **[source](https://github.com/Unity-Technologies/ml-agents/tree/release_12)** | **[docs](https://github.com/Unity-Technologies/ml-agents/tree/release_12_docs/docs/Readme.md)** | **[download](https://github.com/Unity-Technologies/ml-agents/archive/release_12.zip)** |
+| **Release 11** | December 21, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_11) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_11_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_11.zip) |
+| **Release 10** | November 18, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_10) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_10_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_10.zip) |
-| **Release 4** | July 15, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_4) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_4_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_4.zip) |
 
 ## Citation
 
 
 For any other questions or feedback, connect directly with the ML-Agents team at
 ml-agents@unity3d.com.
+
+## Privacy
+
+In order to improve the developer experience for Unity ML-Agents Toolkit, we have added in-editor analytics.
+Please refer to "Information that is passively collected by Unity" in the
+[Unity Privacy Policy](https://unity3d.com/legal/privacy-policy).
 
 ## License
 

com.unity.ml-agents.extensions/Documentation~/Grid-Sensor.md

 
 An image can be thought of as a matrix of a predefined width (W) and a height (H) and each pixel can be thought of as simply an array of length 3 (in the case of RGB), `[Red, Green, Blue]` holding the different channel information of the color (channel) intensities at that pixel location. Thus an image is just a 3 dimensional matrix of size WxHx3. A Grid Observation can be thought of as a generalization of this setup where in place of a pixel there is a "cell" which is an array of length N representing different channel intensities at that cell position. From a Convolutional Neural Network point of view, the introduction of multiple channels in an "image" isn't a new concept. One such example is using an RGB-Depth image which is used in several robotics applications. The distinction of Grid Observations is what the data within the channels represents. Instead of limiting the channels to color intensities, the channels within a cell of a Grid Observation generalize to any data that can be represented by a single number (float or int).
 
-Before jumping into the details of the Grid Sensor, an important thing to note is the agent performance and qualitatively different behavior over raycasts. Unity MLAgent's comes with a suite of example environments. One in particular, the [Food Collector](https://github.com/Unity-Technologies/ml-agents/tree/release_10_docs/docs/Learning-Environment-Examples.md#food-collector), has been the focus of the Grid Sensor development.
+Before jumping into the details of the Grid Sensor, an important thing to note is the agent performance and qualitatively different behavior over raycasts. Unity MLAgent's comes with a suite of example environments. One in particular, the [Food Collector](https://github.com/Unity-Technologies/ml-agents/tree/release_12_docs/docs/Learning-Environment-Examples.md#food-collector), has been the focus of the Grid Sensor development.
 
 The Food Collector environment can be described as:
 * Set-up: A multi-agent environment where agents compete to collect food.

com.unity.ml-agents.extensions/Documentation~/Match3.md

 This implementation includes:
 
 * C# implementation catered toward a Match-3 setup including concepts around encoding for moves based on [Human Like Playtesting with Deep Learning](https://www.researchgate.net/publication/328307928_Human-Like_Playtesting_with_Deep_Learning)
-* An example Match-3 scene with ML-Agents implemented (located under /Project/Assets/ML-Agents/Examples/Match3). More information, on Match-3 example [here](https://github.com/Unity-Technologies/ml-agents/tree/release_10_docs/docs/docs/Learning-Environment-Examples.md#match-3).
+* An example Match-3 scene with ML-Agents implemented (located under /Project/Assets/ML-Agents/Examples/Match3). More information, on Match-3 example [here](https://github.com/Unity-Technologies/ml-agents/tree/release_12_docs/docs/docs/Learning-Environment-Examples.md#match-3).
 
 ### Feedback
 If you are a Match-3 developer and are trying to leverage ML-Agents for this scenario, [we want to hear from you](https://forms.gle/TBsB9jc8WshgzViU9). Additionally, we are also looking for interested Match-3 teams to speak with us for 45 minutes. If you are interested, please indicate that in the [form](https://forms.gle/TBsB9jc8WshgzViU9).  If selected, we will provide gift cards as a token of appreciation.

com.unity.ml-agents.extensions/Documentation~/com.unity.ml-agents.extensions.md

 recommended ways to install the package:
 
 ### Local Installation
-[Clone the repository](https://github.com/Unity-Technologies/ml-agents/tree/release_10_docs/docs/Installation.md#clone-the-ml-agents-toolkit-repository-optional) and follow the
-[Local Installation for Development](https://github.com/Unity-Technologies/ml-agents/tree/release_10_docs/docs/Installation.md#advanced-local-installation-for-development-1)
+[Clone the repository](https://github.com/Unity-Technologies/ml-agents/tree/release_12_docs/docs/Installation.md#clone-the-ml-agents-toolkit-repository-optional) and follow the
+[Local Installation for Development](https://github.com/Unity-Technologies/ml-agents/tree/release_12_docs/docs/Installation.md#advanced-local-installation-for-development-1)
-![Package Manager git URL](https://github.com/Unity-Technologies/ml-agents/tree/release_10_docs/docs/images/unity_package_manager_git_url.png)
+![Package Manager git URL](https://github.com/Unity-Technologies/ml-agents/tree/release_12_docs/docs/images/unity_package_manager_git_url.png)
 In the dialog that appears, enter
  ```
  git+https://github.com/Unity-Technologies/ml-agents.git?path=com.unity.ml-agents.extensions
 none
 
 ## Need Help?
-The main [README](https://github.com/Unity-Technologies/ml-agents/tree/release_10_docs/README.md) contains links for contacting the team or getting support.
+The main [README](https://github.com/Unity-Technologies/ml-agents/tree/release_12_docs/README.md) contains links for contacting the team or getting support.

com.unity.ml-agents.extensions/Editor/EditorExample.cs

-using Unity.MLAgents;
-
 namespace Unity.MLAgents.Extensions.Editor
 {
 

com.unity.ml-agents.extensions/Editor/RigidBodySensorComponentEditor.cs

-using UnityEngine;
 using UnityEditor;
 using Unity.MLAgents.Editor;
 using Unity.MLAgents.Extensions.Sensors;

com.unity.ml-agents.extensions/Runtime/Match3/Match3ActuatorComponent.cs

         /// </summary>
         [FormerlySerializedAs("ForceRandom")]
         [Tooltip("Force using the Agent's Heuristic() method to decide the action. This should only be used in testing.")]
-        public bool ForceHeuristic = false;
+        public bool ForceHeuristic;
 
         /// <inheritdoc/>
         public override IActuator CreateActuator()

com.unity.ml-agents.extensions/Runtime/Sensors/CountingGridSensor.cs

 using System;
 using UnityEngine;
-using UnityEngine.Assertions;
 
 namespace Unity.MLAgents.Extensions.Sensors
 {

com.unity.ml-agents.extensions/Runtime/Sensors/PhysicsBodySensor.cs

 using System.Collections.Generic;
+#if UNITY_2020_1_OR_NEWER
+#endif
 using Unity.MLAgents.Sensors;
 
 namespace Unity.MLAgents.Extensions.Sensors

com.unity.ml-agents.extensions/Runtime/Sensors/RigidBodyJointExtractor.cs

-using System.Collections.Generic;
 using UnityEngine;
 using Unity.MLAgents.Sensors;
 

com.unity.ml-agents.extensions/Tests/Editor/Match3/AbstractBoardTests.cs

         public override int GetCellType(int row, int col)
         {
             var character = m_Board[m_Board.Length - 1 - row][col];
-            return (int)(character - '0');
+            return (character - '0');
-            return (int)(character - '0');
+            return (character - '0');
         }
 
     }

com.unity.ml-agents.extensions/Tests/Editor/Match3/Match3SensorTests.cs

-using System;
-using Unity.MLAgents.Actuators;
 using Unity.MLAgents.Extensions.Match3;
 using UnityEngine;
 using Unity.MLAgents.Extensions.Tests.Sensors;

com.unity.ml-agents.extensions/Tests/Editor/Sensors/ChannelHotPerceiveTests.cs

         {
             string[] tags = { k_Tag1 };
             int[] depths = { 1 };
-            dummyData.Data = new float[] { -0.1f };
+            dummyData.Data = new[] { -0.1f };
             Color[] colors = { Color.red, Color.magenta };
             gridSensor.SetParameters(tags, depths, GridSensor.GridDepthType.ChannelHot,
                 1f, 1f, 10, 10, LayerMask.GetMask("Default"), false, colors);
         {
             string[] tags = { k_Tag1 };
             int[] depths = { 1 };
-            dummyData.Data = new float[] { 1.1f };
+            dummyData.Data = new[] { 1.1f };
             Color[] colors = { Color.red, Color.magenta };
             gridSensor.SetParameters(tags, depths, GridSensor.GridDepthType.ChannelHot,
                 1f, 1f, 10, 10, LayerMask.GetMask("Default"), false, colors);
         {
             string[] tags = { k_Tag1 };
             int[] depths = { 1 };
-            dummyData.Data = new float[] { .2f };
+            dummyData.Data = new[] { .2f };
             Color[] colors = { Color.red, Color.magenta };
             gridSensor.SetParameters(tags, depths, GridSensor.GridDepthType.ChannelHot,
                 1f, 1f, 10, 10, LayerMask.GetMask("Default"), false, colors);
             Assert.AreEqual(10 * 10 * 1, output.Length);
 
             int[] subarrayIndicies = new int[] { 77, 78, 87, 88 };
-            float[][] expectedSubarrays = GridObsTestUtils.DuplicateArray(new float[] { .2f }, 4);
-            float[] expectedDefault = new float[] { 0 };
+            float[][] expectedSubarrays = GridObsTestUtils.DuplicateArray(new[] { .2f }, 4);
+            float[] expectedDefault = new[] { 0.0f };
             GridObsTestUtils.AssertSubarraysAtIndex(output, subarrayIndicies, expectedSubarrays, expectedDefault);
         }
 
             string[] tags = { k_Tag1 };
             int[] depths = { 3 };
-            dummyData.Data = new float[] { -1f };
+            dummyData.Data = new[] { -1f };
             Color[] colors = { Color.red, Color.magenta };
             gridSensor.SetParameters(tags, depths, GridSensor.GridDepthType.ChannelHot,
                 1f, 1f, 10, 10, LayerMask.GetMask("Default"), false, colors);
         {
             string[] tags = { k_Tag1 };
             int[] depths = { 3 };
-            dummyData.Data = new float[] { 4f };
+            dummyData.Data = new[] { 4f };
             Color[] colors = { Color.red, Color.magenta };
             gridSensor.SetParameters(tags, depths, GridSensor.GridDepthType.ChannelHot,
                 1f, 1f, 10, 10, LayerMask.GetMask("Default"), false, colors);
         {
             string[] tags = { k_Tag1 };
             int[] depths = { 3 };
-            dummyData.Data = new float[] { 2f };
+            dummyData.Data = new[] { 2f };
             Color[] colors = { Color.red, Color.magenta };
             gridSensor.SetParameters(tags, depths, GridSensor.GridDepthType.ChannelHot,
                 1f, 1f, 10, 10, LayerMask.GetMask("Default"), false, colors);
 
             Assert.AreEqual(10 * 10 * 3, output.Length);
 
-            int[] subarrayIndicies = new int[] { 77, 78, 87, 88 };
+            int[] subarrayIndicies = new[] { 77, 78, 87, 88 };
-            float[] expectedDefault = new float[] { 1, 0, 0 };
+            float[] expectedDefault = new[] { 1.0f, 0.0f, 0.0f };
             GridObsTestUtils.AssertSubarraysAtIndex(output, subarrayIndicies, expectedSubarrays, expectedDefault);
         }
 
             string[] tags = { k_Tag1 };
             int[] depths = { 3 };
-            dummyData.Data = new float[] { 2.4f };
+            dummyData.Data = new[] { 2.4f };
             Color[] colors = { Color.red, Color.magenta };
             gridSensor.SetParameters(tags, depths, GridSensor.GridDepthType.ChannelHot,
                 1f, 1f, 10, 10, LayerMask.GetMask("Default"), false, colors);
         {
             string[] tags = { k_Tag1 };
             int[] depths = { 1, 1 };
-            dummyData.Data = new float[] { .4f, .3f };
+            dummyData.Data = new[] { .4f, .3f };
             Color[] colors = { Color.red, Color.magenta };
             gridSensor.SetParameters(tags, depths, GridSensor.GridDepthType.ChannelHot,
                 1f, 1f, 10, 10, LayerMask.GetMask("Default"), false, colors);
             Assert.AreEqual(10 * 10 * 2, output.Length);
 
             int[] subarrayIndicies = new int[] { 77, 78, 87, 88 };
-            float[][] expectedSubarrays = GridObsTestUtils.DuplicateArray(new float[] { .4f, .3f }, 4);
+            float[][] expectedSubarrays = GridObsTestUtils.DuplicateArray(new[] { .4f, .3f }, 4);
             float[] expectedDefault = new float[] { 0, 0 };
             GridObsTestUtils.AssertSubarraysAtIndex(output, subarrayIndicies, expectedSubarrays, expectedDefault);
         }
         {
             string[] tags = { k_Tag1 };
             int[] depths = { 1, 3 };
-            dummyData.Data = new float[] { .4f, 4f };
+            dummyData.Data = new[] { .4f, 4f };
             Color[] colors = { Color.red, Color.magenta };
             gridSensor.SetParameters(tags, depths, GridSensor.GridDepthType.ChannelHot,
                 1f, 1f, 10, 10, LayerMask.GetMask("Default"), false, colors);
         {
             string[] tags = { k_Tag1 };
             int[] depths = { 1, 3 };
-            dummyData.Data = new float[] { .4f, 1f };
+            dummyData.Data = new[] { .4f, 1f };
             Color[] colors = { Color.red, Color.magenta };
             gridSensor.SetParameters(tags, depths, GridSensor.GridDepthType.ChannelHot,
                 1f, 1f, 10, 10, LayerMask.GetMask("Default"), false, colors);
             Assert.AreEqual(10 * 10 * 4, output.Length);
 
             int[] subarrayIndicies = new int[] { 77, 78, 87, 88 };
-            float[][] expectedSubarrays = GridObsTestUtils.DuplicateArray(new float[] { .4f, 0, 1, 0 }, 4);
+            float[][] expectedSubarrays = GridObsTestUtils.DuplicateArray(new[] { .4f, 0, 1, 0 }, 4);
             float[] expectedDefault = new float[] { 0, 1, 0, 0 };
             GridObsTestUtils.AssertSubarraysAtIndex(output, subarrayIndicies, expectedSubarrays, expectedDefault);
         }
         {
             string[] tags = { k_Tag1 };
             int[] depths = { 3, 1 };
-            dummyData.Data = new float[] { 1f, .4f };
+            dummyData.Data = new[] { 1f, .4f };
             Color[] colors = { Color.red, Color.magenta };
             gridSensor.SetParameters(tags, depths, GridSensor.GridDepthType.ChannelHot,
                 1f, 1f, 10, 10, LayerMask.GetMask("Default"), false, colors);
 
             Assert.AreEqual(10 * 10 * 4, output.Length);
 
-            int[] subarrayIndicies = new int[] { 77, 78, 87, 88 };
-            float[][] expectedSubarrays = GridObsTestUtils.DuplicateArray(new float[] { 0, 1, 0, .4f }, 4);
+            int[] subarrayIndicies = new[] { 77, 78, 87, 88 };
+            float[][] expectedSubarrays = GridObsTestUtils.DuplicateArray(new[] { 0, 1, 0, .4f }, 4);
             float[] expectedDefault = new float[] { 1, 0, 0, 0 };
             GridObsTestUtils.AssertSubarraysAtIndex(output, subarrayIndicies, expectedSubarrays, expectedDefault);
         }
         {
             string[] tags = { k_Tag1 };
             int[] depths = { 3, 3 };
-            dummyData.Data = new float[] { 1f, 2.2f };
+            dummyData.Data = new[] { 1f, 2.2f };
             Color[] colors = { Color.red, Color.magenta };
             gridSensor.SetParameters(tags, depths, GridSensor.GridDepthType.ChannelHot,
                 1f, 1f, 10, 10, LayerMask.GetMask("Default"), false, colors);
         {
             string[] tags = { k_Tag1 };
             int[] depths = { 5, 1, 3 };
-            dummyData.Data = new float[] { 3f, .6f, 2.2f };
+            dummyData.Data = new[] { 3f, .6f, 2.2f };
             Color[] colors = { Color.red, Color.magenta };
             gridSensor.SetParameters(tags, depths, GridSensor.GridDepthType.ChannelHot,
                 1f, 1f, 10, 10, LayerMask.GetMask("Default"), false, colors);
             Assert.AreEqual(10 * 10 * 9, output.Length);
 
             int[] subarrayIndicies = new int[] { 77, 78, 87, 88 };
-            float[][] expectedSubarrays = GridObsTestUtils.DuplicateArray(new float[] { 0, 0, 0, 1, 0, .6f, 0, 0, 1 }, 4);
+            float[][] expectedSubarrays = GridObsTestUtils.DuplicateArray(new[] { 0, 0, 0, 1, 0, .6f, 0, 0, 1 }, 4);
             float[] expectedDefault = new float[] { 1, 0, 0, 0, 0, 0, 1, 0, 0 };
             GridObsTestUtils.AssertSubarraysAtIndex(output, subarrayIndicies, expectedSubarrays, expectedDefault);
         }
         {
             string[] tags = { k_Tag1 };
             int[] depths = { 5, 1, 3 };
-            dummyData.Data = new float[] { 3f, .6f, 2.2f };
+            dummyData.Data = new[] { 3f, .6f, 2.2f };
             Color[] colors = { Color.red, Color.magenta };
             gridSensor.SetParameters(tags, depths, GridSensor.GridDepthType.ChannelHot,
                 1f, 1f, 10, 10, LayerMask.GetMask("Default"), false, colors);
             Assert.AreEqual(10 * 10 * 9, output.Length);
 
             int[] subarrayIndicies = new int[] { 77, 78, 87, 88 };
-            float[][] expectedSubarrays = GridObsTestUtils.DuplicateArray(new float[] { 0, 0, 0, 1, 0, .6f, 0, 0, 1 }, 4);
+            float[][] expectedSubarrays = GridObsTestUtils.DuplicateArray(new[] { 0, 0, 0, 1, 0, .6f, 0, 0, 1 }, 4);
             float[] expectedDefault = new float[] { 1, 0, 0, 0, 0, 0, 1, 0, 0 };
             GridObsTestUtils.AssertSubarraysAtIndex(output, subarrayIndicies, expectedSubarrays, expectedDefault);
 

com.unity.ml-agents.extensions/Tests/Editor/Sensors/ChannelHotShapeTests.cs

 using NUnit.Framework;
 using UnityEngine;
 using Unity.MLAgents.Extensions.Sensors;
-using NUnit.Framework.Internal;
-using UnityEngine.TestTools;
-using System.Collections;
 
 namespace Unity.MLAgents.Extensions.Tests.Sensors
 {

com.unity.ml-agents.extensions/Tests/Editor/Sensors/ChannelPerceiveTests.cs

 using UnityEngine.TestTools;
 using Unity.MLAgents.Extensions.Sensors;
 using Unity.MLAgents.Extensions.TestUtils.Sensors;
-using System.Linq;
 
 namespace Unity.MLAgents.Extensions.Tests.Sensors
 {
         {
             string[] tags = { k_Tag1 };
             int[] depths = { 1 };
-            dummyData.Data = new float[] { -0.1f };
+            dummyData.Data = new[] { -0.1f };
             Color[] colors = { Color.red, Color.magenta };
             gridSensor.SetParameters(tags, depths, GridSensor.GridDepthType.Channel,
                 1f, 1f, 10, 10, LayerMask.GetMask("Default"), false, colors);
         {
             string[] tags = { k_Tag1 };
             int[] depths = { 1 };
-            dummyData.Data = new float[] { 1.1f };
+            dummyData.Data = new[] { 1.1f };
             Color[] colors = { Color.red, Color.magenta };
             gridSensor.SetParameters(tags, depths, GridSensor.GridDepthType.Channel,
                 1f, 1f, 10, 10, LayerMask.GetMask("Default"), false, colors);
         {
             string[] tags = { k_Tag1 };
             int[] depths = { 1 };
-            dummyData.Data = new float[] { .2f };
+            dummyData.Data = new[] { .2f };
             Color[] colors = { Color.red, Color.magenta };
             gridSensor.SetParameters(tags, depths, GridSensor.GridDepthType.Channel,
                 1f, 1f, 10, 10, LayerMask.GetMask("Default"), false, colors);
             Assert.AreEqual(10 * 10 * 1, output.Length);
 
             int[] subarrayIndicies = new int