Merge branch 'master' of https://github.com/Unity-Technologies/ml-agents

.pre-commit-config.yaml

     hooks:
     -   id: python-check-mock-methods
 
--   repo: https://github.com/pre-commit/mirrors-pylint
-    rev: v2.4.4
-    hooks:
-    -   id: pylint
-        exclude: >
-            (?x)^(
-                .*_pb2.py|
-                .*_pb2_grpc.py|
-                .*/tests/.*
-            )$
-        args: [--score=n]
-
 - repo: https://github.com/mattlqx/pre-commit-search-and-replace
   rev: v1.0.3
   hooks:

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

 pack:
   name: Pack
   agent:
-    type: Unity::VM::osx
-    image: package-ci/mac:stable
+    type: Unity::VM
+    image: package-ci/ubuntu:stable
     flavor: b1.small
   commands:
     - npm install upm-ci-utils@stable -g --registry https://artifactory.prd.cds.internal.unity3d.com/artifactory/api/npm/upm-npm

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

   variables:
     UNITY_VERSION: {{ editor.version }}
   commands:
-    - python -m pip install unity-downloader-cli --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple --upgrade
+    - python3 -m pip install unity-downloader-cli --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple --upgrade
-    - curl -s https://artifactory.internal.unity3d.com/core-automation/tools/utr-standalone/utr --output utr
+    - curl -s https://artifactory.prd.it.unity3d.com/artifactory/unity-tools-local/utr-standalone/utr --output utr
-    expression: |
-      (pull_request.target eq "master" OR
-      pull_request.target match "release.+") AND
-      NOT pull_request.draft AND
-      (pull_request.changes.any match "com.unity.ml-agents/**" OR
-      pull_request.changes.any match "DevProject/**" OR
-      pull_request.changes.any match "ml-agents/**" OR
-      pull_request.changes.any match "ml-agents-envs/**" OR
-      pull_request.changes.any match ".yamato/com.unity.ml-agents-performance.yml") AND
-      NOT pull_request.changes.all match "**/*.md"
+    recurring:
+      - branch: master
+        frequency: daily
   artifacts:
     logs:
       paths:

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

     - .yamato/com.unity.ml-agents-pack.yml#pack
   triggers:
     cancel_old_ci: true
-    {% if platform.name == "mac" %}
+    {% if platform.name == "linux" %}
     expression: |
       (pull_request.target eq "master" OR
       pull_request.target match "release.+") AND
     image: {{ platform.image }}
     flavor: {{ platform.flavor}}
   commands:
-    - python -m pip install unity-downloader-cli --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple --upgrade
+    - python3 -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-create-project-arg="-upmNoDefaultPackages" --extra-utr-arg "reruncount=2"

.yamato/compressed-sensor-test.yml

 test_compressed_obs_{{ editor.version }}:
   name: Test Compressed Sensor Observation {{ editor.version }}
   agent:
-    type: Unity::VM::osx
-    image: ml-agents/ml-agents-bokken-mac:0.1.4-492264
-    flavor: b1.small
+    type: Unity::VM
+    image: package-ci/ubuntu:stable
+    flavor: b1.medium
-    - pip install pyyaml --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple
-    - python -u -m ml-agents.tests.yamato.setup_venv
-    - ./venv/bin/python ml-agents/tests/yamato/scripts/run_compressed_sensor.py --env=artifacts/testPlayer-TestGridCompressed
-    - ./venv/bin/python ml-agents/tests/yamato/scripts/run_compressed_sensor.py --env=artifacts/testPlayer-TestTextureCompressed
+    - |
+      sudo apt-get update && sudo apt-get install -y python3-venv
+      python3 -m venv venv && source venv/bin/activate
+      python -m pip install pyyaml --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple
+      python -u -m ml-agents.tests.yamato.setup_venv
+      python ml-agents/tests/yamato/scripts/run_compressed_sensor.py --env=artifacts/testPlayer-TestGridCompressed
+      python ml-agents/tests/yamato/scripts/run_compressed_sensor.py --env=artifacts/testPlayer-TestTextureCompressed
-    - .yamato/standalone-build-test.yml#test_mac_standalone_{{ editor.version }}
+    - .yamato/standalone-build-test.yml#test_linux_standalone_{{ editor.version }}
   triggers:
     cancel_old_ci: true
     expression: |

.yamato/gym-interface-test.yml

 ---
 {% for editor in test_editors %}
 test_gym_interface_{{ editor.version }}:
-  name: Test Mac Gym Interface {{ editor.version }}
+  name: Test Linux Gym Interface {{ editor.version }}
-    type: Unity::VM::osx
-    image: ml-agents/ml-agents-bokken-mac:0.1.4-492264
-    flavor: b1.small
+    type: Unity::VM
+    image: package-ci/ubuntu:stable
+    flavor: b1.medium
-    - pip install pyyaml --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple
-    - python -u -m ml-agents.tests.yamato.setup_venv
-    - ./venv/bin/python ml-agents/tests/yamato/scripts/run_gym.py --env=artifacts/testPlayer-Basic
+    - |
+      sudo apt-get update && sudo apt-get install -y python3-venv
+      python3 -m venv venv && source venv/bin/activate
+      python -m pip install pyyaml --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple
+      python -u -m ml-agents.tests.yamato.setup_venv
+      python ml-agents/tests/yamato/scripts/run_gym.py --env=artifacts/testPlayer-Basic
-    - .yamato/standalone-build-test.yml#test_mac_standalone_{{ editor.version }}
+    - .yamato/standalone-build-test.yml#test_linux_standalone_{{ editor.version }}
   triggers:
     cancel_old_ci: true
     expression: |

.yamato/protobuf-generation-test.yml

-test_mac_protobuf_generation:
+test_linux_protobuf_generation:
-    type: Unity::VM::osx
-    image: package-ci/mac:stable
-    flavor: b1.small
+    type: Unity::VM
+    image: package-ci/ubuntu:stable
+    flavor: b1.large
-    HOMEBREW_NO_AUTO_UPDATE: "1"
-      brew install nuget
+      sudo apt-get update && sudo apt-get install -y python3-venv nuget
+      python3 -m venv venv && source venv/bin/activate
-      python3 -m venv venv
-      . venv/bin/activate
-      pip install --upgrade pip --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple
-      pip install grpcio==1.28.1 grpcio-tools==1.13.0 protobuf==3.11.3 six==1.14.0 mypy-protobuf==1.16.0  --progress-bar=off --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple
-      cd protobuf-definitions
-      chmod +x Grpc.Tools.$GRPC_VERSION/tools/macosx_x64/protoc
-      chmod +x Grpc.Tools.$GRPC_VERSION/tools/macosx_x64/grpc_csharp_plugin
-      COMPILER=Grpc.Tools.$GRPC_VERSION/tools/macosx_x64 ./make.sh
+      python3 -m pip install --upgrade pip --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple
+      python3 -m pip install grpcio==1.28.1 grpcio-tools==1.13.0 protobuf==3.11.3 six==1.14.0 mypy-protobuf==1.16.0  --progress-bar=off --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple
+      pushd protobuf-definitions
+      chmod +x Grpc.Tools.$GRPC_VERSION/tools/linux_x64/protoc
+      chmod +x Grpc.Tools.$GRPC_VERSION/tools/linux_x64/grpc_csharp_plugin
+      COMPILER=Grpc.Tools.$GRPC_VERSION/tools/linux_x64 ./make.sh
+      popd
       mkdir -p artifacts
       touch artifacts/proto.patch
       git diff --exit-code -- :/ ":(exclude,top)$CS_PROTO_PATH/*.meta"                          \
       pull_request.target match "release.+") AND
       NOT pull_request.draft AND
       (pull_request.changes.any match "protobuf-definitions/**" OR
+      pull_request.changes.any match "com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/**" OR
+      pull_request.changes.any match "ml-agents-envs/mlagents_envs/communicator_objects/**" OR
       pull_request.changes.any match ".yamato/protobuf-generation-test.yml") AND
       NOT pull_request.changes.all match "protobuf-definitions/**/*.md"
   artifacts:

.yamato/python-ll-api-test.yml

 {% metadata_file .yamato/test_versions.metafile %}
 ---
 {% for editor in test_editors %}
-test_mac_ll_api_{{ editor.version }}:
-  name: Test Mac LL-API {{ editor.version }}
+test_linux_ll_api_{{ editor.version }}:
+  name: Test Linux LL-API {{ editor.version }}
-    type: Unity::VM::osx
-    image: ml-agents/ml-agents-bokken-mac:0.1.4-492264
-    flavor: b1.small
+    type: Unity::VM
+    image: package-ci/ubuntu:stable
+    flavor: b1.medium
-    - pip install pyyaml --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple
-    - python -u -m ml-agents.tests.yamato.setup_venv
-    - ./venv/bin/python ml-agents/tests/yamato/scripts/run_llapi.py
-    - ./venv/bin/python ml-agents/tests/yamato/scripts/run_llapi.py --env=artifacts/testPlayer-Basic
-    - ./venv/bin/python ml-agents/tests/yamato/scripts/run_llapi.py --env=artifacts/testPlayer-WallJump
-    - ./venv/bin/python ml-agents/tests/yamato/scripts/run_llapi.py --env=artifacts/testPlayer-Bouncer
+    - |
+      sudo apt-get update && sudo apt-get install -y python3-venv
+      python3 -m venv venv && source venv/bin/activate
+      python -m pip install pyyaml --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple
+      python -u -m ml-agents.tests.yamato.setup_venv
+      python ml-agents/tests/yamato/scripts/run_llapi.py
+      python ml-agents/tests/yamato/scripts/run_llapi.py --env=artifacts/testPlayer-Basic
+      python ml-agents/tests/yamato/scripts/run_llapi.py --env=artifacts/testPlayer-WallJump
+      python ml-agents/tests/yamato/scripts/run_llapi.py --env=artifacts/testPlayer-Bouncer
-    - .yamato/standalone-build-test.yml#test_mac_standalone_{{ editor.version }}
+    - .yamato/standalone-build-test.yml#test_linux_standalone_{{ editor.version }}
   triggers:
     cancel_old_ci: true
     expression: |

.yamato/standalone-build-test.yml

 {% metadata_file .yamato/test_versions.metafile %}
 ---
 {% for editor in test_editors %}
-test_mac_standalone_{{ editor.version }}:
-  name: Test Mac Standalone {{ editor.version }}
+test_linux_standalone_{{ editor.version }}:
+  name: Test Linux Standalone {{ editor.version }}
-    type: Unity::VM::osx
-    image: ml-agents/ml-agents-bokken-mac:0.1.4-492264
-    flavor: i1.small
+    type: Unity::VM
+    image: package-ci/ubuntu:stable
+    flavor: b1.large
-    - pip install pyyaml --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple
-    # TODO remove the "--user" command and the path prefix when we can migrate away from the custom bokken image
-    - python -m pip install unity-downloader-cli --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple --upgrade --user
-    - /Users/bokken/Library/Python/3.7/bin/unity-downloader-cli -u {{ editor.version }} -c editor --wait --fast
-    - python -u -m ml-agents.tests.yamato.standalone_build_tests
-    - python -u -m ml-agents.tests.yamato.standalone_build_tests --scene=Assets/ML-Agents/Examples/Basic/Scenes/Basic.unity
-    - python -u -m ml-agents.tests.yamato.standalone_build_tests --scene=Assets/ML-Agents/Examples/Bouncer/Scenes/Bouncer.unity
-    - python -u -m ml-agents.tests.yamato.standalone_build_tests --scene=Assets/ML-Agents/Examples/WallJump/Scenes/WallJump.unity
-    - python -u -m ml-agents.tests.yamato.standalone_build_tests --scene=Assets/ML-Agents/TestScenes/TestCompressedGrid/TestGridCompressed.unity
-    - python -u -m ml-agents.tests.yamato.standalone_build_tests --scene=Assets/ML-Agents/TestScenes/TestCompressedTexture/TestTextureCompressed.unity
+    - python3 -m pip install pyyaml --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple
+    - python3 -m pip install unity-downloader-cli --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple --upgrade
+    - unity-downloader-cli -u {{ editor.version }} -c editor --wait --fast
+    - python3 -u -m ml-agents.tests.yamato.standalone_build_tests --build-target=linux
+    - python3 -u -m ml-agents.tests.yamato.standalone_build_tests --build-target=linux --scene=Assets/ML-Agents/Examples/Basic/Scenes/Basic.unity
+    - python3 -u -m ml-agents.tests.yamato.standalone_build_tests --build-target=linux --scene=Assets/ML-Agents/Examples/Bouncer/Scenes/Bouncer.unity
+    - python3 -u -m ml-agents.tests.yamato.standalone_build_tests --build-target=linux --scene=Assets/ML-Agents/Examples/WallJump/Scenes/WallJump.unity
+    - python3 -u -m ml-agents.tests.yamato.standalone_build_tests --build-target=linux --scene=Assets/ML-Agents/TestScenes/TestCompressedGrid/TestGridCompressed.unity
+    - python3 -u -m ml-agents.tests.yamato.standalone_build_tests --build-target=linux --scene=Assets/ML-Agents/TestScenes/TestCompressedTexture/TestTextureCompressed.unity
   triggers:
     cancel_old_ci: true
     expression: |
     standalonebuild:
       paths:
         - "artifacts/testPlayer*/**"
+        - "artifacts/**/UnityPlayer.so"
 {% endfor %}

.yamato/training-int-tests.yml

 {% metadata_file .yamato/test_versions.metafile %}
 ---
 {% for editor in test_editors %}
-test_mac_training_int_{{ editor.version }}:
-  name: Test Mac Fast Training {{ editor.version }}
+test_linux_training_int_{{ editor.version }}:
+  name: Test Linux Fast Training {{ editor.version }}
-    type: Unity::VM::osx
-    image: ml-agents/ml-agents-bokken-mac:0.1.4-492264
-    flavor: b1.small
+    type: Unity::VM
+    image: package-ci/ubuntu:stable
+    flavor: b1.medium
-    - pip install pyyaml --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple
-    # TODO remove the "--user" command and the path prefix when we can migrate away from the custom bokken image
-    - python -m pip install unity-downloader-cli --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple --upgrade --user
-    - /Users/bokken/Library/Python/3.7/bin/unity-downloader-cli -u {{ editor.version }} -c editor --wait --fast
-    - python -u -m ml-agents.tests.yamato.training_int_tests
-    # Backwards-compatibility tests.
-    # If we make a breaking change to the communication protocol, these will need
-    # to be disabled until the next release.
-    - python -u -m ml-agents.tests.yamato.training_int_tests --python=0.16.0
-    - python -u -m ml-agents.tests.yamato.training_int_tests --csharp={{ editor.csharp_backcompat_version }}
+    - |
+      sudo apt-get update && sudo apt-get install -y python3-venv
+      python3 -m venv venv && source venv/bin/activate
+      python -m pip install pyyaml --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple
+      python -u -m ml-agents.tests.yamato.training_int_tests
-    - .yamato/standalone-build-test.yml#test_mac_standalone_{{ editor.version }}
+    - .yamato/standalone-build-test.yml#test_linux_standalone_{{ editor.version }}
   triggers:
     cancel_old_ci: true
     expression: |
         - "artifacts/inference.onnx.txt"
     standalonebuild:
       paths:
-        - "artifacts/testplayer*/**"
+        - "artifacts/testPlayer*/**"
         - "artifacts/models/**"
 {% endfor %}

Project/Assets/ML-Agents/Editor/Tests/StandaloneBuildTest.cs

     {
         const string k_OutputCommandLineFlag = "--mlagents-build-output-path";
         const string k_SceneCommandLineFlag = "--mlagents-build-scene-path";
+        private const string k_BuildTargetFlag = "--mlagents-build-target";
 
         public static void BuildStandalonePlayerOSX()
         {
+            var buildTarget = BuildTarget.StandaloneOSX;
 
             var args = Environment.GetCommandLineArgs();
             for (var i = 0; i < args.Length - 1; i++)
                 {
                     scenePath = args[i + 1];
                 }
+                else if (args[i] == k_BuildTargetFlag)
+                {
+                    buildTarget = (BuildTarget)Enum.Parse(typeof(BuildTarget), args[i + 1], ignoreCase: true);
+                }
             }
 
             string[] scenes = { scenePath };
-                BuildTarget.StandaloneOSX,
+                buildTarget,
                 BuildOptions.None
             );
             var isOk = buildResult.summary.result == BuildResult.Succeeded;

Project/Assets/ML-Agents/Examples/Hallway/Scripts/HallwayAgent.cs

     Renderer m_GroundRenderer;
     HallwaySettings m_HallwaySettings;
     int m_Selection;
+    StatsRecorder m_statsRecorder;
 
     public override void Initialize()
     {
         m_GroundMaterial = m_GroundRenderer.material;
+        m_statsRecorder = Academy.Instance.StatsRecorder;
     }
 
     public override void CollectObservations(VectorSensor sensor)
             {
                 SetReward(1f);
                 StartCoroutine(GoalScoredSwapGroundMaterial(m_HallwaySettings.goalScoredMaterial, 0.5f));
+                m_statsRecorder.Add("Goal/Correct", 1, StatAggregationMethod.Sum);
+                m_statsRecorder.Add("Goal/Wrong", 1, StatAggregationMethod.Sum);
             }
             EndEpisode();
         }
             symbolXGoal.transform.position = new Vector3(7f, 0.5f, 22.29f) + area.transform.position;
             symbolOGoal.transform.position = new Vector3(-7f, 0.5f, 22.29f) + area.transform.position;
         }
+        m_statsRecorder.Add("Goal/Correct", 0, StatAggregationMethod.Sum);
+        m_statsRecorder.Add("Goal/Wrong", 0, StatAggregationMethod.Sum);
     }
 }

README.md

 
 ## Additional Resources
 
-We have published a series of blog posts that are relevant for ML-Agents:
+We have a Unity Learn course,
+[ML-Agents: Hummingsbird](https://learn.unity.com/course/ml-agents-hummingbirds),
+that provides a gentle introduction to Unity and the ML-Agents Toolkit.
+
+We've also partnered with
+[CodeMonkeyUnity](https://www.youtube.com/c/CodeMonkeyUnity) to create a
+[series of tutorial videos](https://www.youtube.com/playlist?list=PLzDRvYVwl53vehwiN_odYJkPBzcqFw110)
+on how to implement and use the ML-Agents Toolkit.
+
+We have also published a series of blog posts that are relevant for ML-Agents:
+- (December 28, 2020)
+  [Happy holidays from the Unity ML-Agents team!](https://blogs.unity3d.com/2020/12/28/happy-holidays-from-the-unity-ml-agents-team/)
+- (November 20, 2020)
+  [How Eidos-Montréal created Grid Sensors to improve observations for training agents](https://blogs.unity3d.com/2020/11/20/how-eidos-montreal-created-grid-sensors-to-improve-observations-for-training-agents/)
+- (November 11, 2020)
+  [2020 AI@Unity interns shoutout](https://blogs.unity3d.com/2020/11/11/2020-aiunity-interns-shoutout/)
 - (May 12, 2020)
   [Announcing ML-Agents Unity Package v1.0!](https://blogs.unity3d.com/2020/05/12/announcing-ml-agents-unity-package-v1-0/)
 - (February 28, 2020)
   ([multi-armed bandit](https://blogs.unity3d.com/2017/06/26/unity-ai-themed-blog-entries/)
   and
   [Q-learning](https://blogs.unity3d.com/2017/08/22/unity-ai-reinforcement-learning-with-q-learning/))
-
-In addition to our own documentation, here are some additional, relevant
-articles:
-
-- [A Game Developer Learns Machine Learning](https://mikecann.co.uk/machine-learning/a-game-developer-learns-machine-learning-intent/)
-- [Explore Unity Technologies ML-Agents Exclusively on Intel Architecture](https://software.intel.com/en-us/articles/explore-unity-technologies-ml-agents-exclusively-on-intel-architecture)
-- [ML-Agents Penguins tutorial](https://learn.unity.com/project/ml-agents-penguins)
 
 ## Community and Feedback
 

com.unity.ml-agents/CHANGELOG.md

 
 ### Minor Changes
 #### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
+- `StatAggregationMethod.Sum` can now be passed to `StatsRecorder.Add()`. This
+will result in the values being summed (instead of averaged) when written to
+TensorBoard. Thanks to @brccabral for the contribution! (#4816)
+- Fix a compile warning about using an obsolete enum in `GrpcExtensions.cs`. (#4812)
+- Fixed a bug that would cause an exception when `RunOptions` was deserialized via `pickle`. (#4842)
 
 
 ## [1.7.2-preview] - 2020-12-22

com.unity.ml-agents/Runtime/Communicator/GrpcExtensions.cs

             ActionSpec actionSpec;
             if (bpp.ActionSpec == null)
             {
+                // Disable deprecation warnings so we can set legacy fields
+#pragma warning disable CS0618
                 var spaceType = (SpaceType)bpp.VectorActionSpaceTypeDeprecated;
                 if (spaceType == SpaceType.Continuous)
                 {
                 {
                     actionSpec = ActionSpec.MakeDiscrete(bpp.VectorActionSizeDeprecated.ToArray());
                 }
+#pragma warning restore CS0618
             }
             else
             {
                 }
             }
             observationProto.Shape.AddRange(shape);
+
+            // Add the observation type, if any, to the observationProto
+            var typeSensor = sensor as ITypedSensor;
+            if (typeSensor != null)
+            {
+                observationProto.ObservationType = (ObservationTypeProto)typeSensor.GetObservationType();
+            }
+            else
+            {
+                observationProto.ObservationType = ObservationTypeProto.Default;
+            }
             return observationProto;
         }
 

com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/Observation.cs

       byte[] descriptorData = global::System.Convert.FromBase64String(
           string.Concat(
             "CjRtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL29ic2VydmF0",
-            "aW9uLnByb3RvEhRjb21tdW5pY2F0b3Jfb2JqZWN0cyK7AgoQT2JzZXJ2YXRp",
+            "aW9uLnByb3RvEhRjb21tdW5pY2F0b3Jfb2JqZWN0cyKBAwoQT2JzZXJ2YXRp",
-            "KAUSHAoUZGltZW5zaW9uX3Byb3BlcnRpZXMYBiADKAUaGQoJRmxvYXREYXRh",
-            "EgwKBGRhdGEYASADKAJCEgoQb2JzZXJ2YXRpb25fZGF0YSopChRDb21wcmVz",
-            "c2lvblR5cGVQcm90bxIICgROT05FEAASBwoDUE5HEAFCJaoCIlVuaXR5Lk1M",
-            "QWdlbnRzLkNvbW11bmljYXRvck9iamVjdHNiBnByb3RvMw=="));
+            "KAUSHAoUZGltZW5zaW9uX3Byb3BlcnRpZXMYBiADKAUSRAoQb2JzZXJ2YXRp",
+            "b25fdHlwZRgHIAEoDjIqLmNvbW11bmljYXRvcl9vYmplY3RzLk9ic2VydmF0",
+            "aW9uVHlwZVByb3RvGhkKCUZsb2F0RGF0YRIMCgRkYXRhGAEgAygCQhIKEG9i",
+            "c2VydmF0aW9uX2RhdGEqKQoUQ29tcHJlc3Npb25UeXBlUHJvdG8SCAoETk9O",
+            "RRAAEgcKA1BORxABKkYKFE9ic2VydmF0aW9uVHlwZVByb3RvEgsKB0RFRkFV",
+            "TFQQABIICgRHT0FMEAESCgoGUkVXQVJEEAISCwoHTUVTU0FHRRADQiWqAiJV",
+            "bml0eS5NTEFnZW50cy5Db21tdW5pY2F0b3JPYmplY3RzYgZwcm90bzM="));
-          new pbr::GeneratedClrTypeInfo(new[] {typeof(global::Unity.MLAgents.CommunicatorObjects.CompressionTypeProto), }, new pbr::GeneratedClrTypeInfo[] {
-            new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.ObservationProto), global::Unity.MLAgents.CommunicatorObjects.ObservationProto.Parser, new[]{ "Shape", "CompressionType", "CompressedData", "FloatData", "CompressedChannelMapping", "DimensionProperties" }, new[]{ "ObservationData" }, null, new pbr::GeneratedClrTypeInfo[] { new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.ObservationProto.Types.FloatData), global::Unity.MLAgents.CommunicatorObjects.ObservationProto.Types.FloatData.Parser, new[]{ "Data" }, null, null, null)})
+          new pbr::GeneratedClrTypeInfo(new[] {typeof(global::Unity.MLAgents.CommunicatorObjects.CompressionTypeProto), typeof(global::Unity.MLAgents.CommunicatorObjects.ObservationTypeProto), }, new pbr::GeneratedClrTypeInfo[] {
+            new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.ObservationProto), global::Unity.MLAgents.CommunicatorObjects.ObservationProto.Parser, new[]{ "Shape", "CompressionType", "CompressedData", "FloatData", "CompressedChannelMapping", "DimensionProperties", "ObservationType" }, new[]{ "ObservationData" }, null, new pbr::GeneratedClrTypeInfo[] { new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.ObservationProto.Types.FloatData), global::Unity.MLAgents.CommunicatorObjects.ObservationProto.Types.FloatData.Parser, new[]{ "Data" }, null, null, null)})
           }));
     }
     #endregion
   internal enum CompressionTypeProto {
     [pbr::OriginalName("NONE")] None = 0,
     [pbr::OriginalName("PNG")] Png = 1,
+  }
+
+  internal enum ObservationTypeProto {
+    [pbr::OriginalName("DEFAULT")] Default = 0,
+    [pbr::OriginalName("GOAL")] Goal = 1,
+    [pbr::OriginalName("REWARD")] Reward = 2,
+    [pbr::OriginalName("MESSAGE")] Message = 3,
   }
 
   #endregion
       compressionType_ = other.compressionType_;
       compressedChannelMapping_ = other.compressedChannelMapping_.Clone();
       dimensionProperties_ = other.dimensionProperties_.Clone();
+      observationType_ = other.observationType_;
       switch (other.ObservationDataCase) {
         case ObservationDataOneofCase.CompressedData:
           CompressedData = other.CompressedData;
       get { return dimensionProperties_; }
     }
 
+    /// <summary>Field number for the "observation_type" field.</summary>
+    public const int ObservationTypeFieldNumber = 7;
+    private global::Unity.MLAgents.CommunicatorObjects.ObservationTypeProto observationType_ = 0;
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public global::Unity.MLAgents.CommunicatorObjects.ObservationTypeProto ObservationType {
+      get { return observationType_; }
+      set {
+        observationType_ = value;
+      }
+    }
+
     private object observationData_;
     /// <summary>Enum of possible cases for the "observation_data" oneof.</summary>
     public enum ObservationDataOneofCase {
       if (!object.Equals(FloatData, other.FloatData)) return false;
       if(!compressedChannelMapping_.Equals(other.compressedChannelMapping_)) return false;
       if(!dimensionProperties_.Equals(other.dimensionProperties_)) return false;
+      if (ObservationType != other.ObservationType) return false;
       if (ObservationDataCase != other.ObservationDataCase) return false;
       return Equals(_unknownFields, other._unknownFields);
     }
       if (observationDataCase_ == ObservationDataOneofCase.FloatData) hash ^= FloatData.GetHashCode();
       hash ^= compressedChannelMapping_.GetHashCode();
       hash ^= dimensionProperties_.GetHashCode();
+      if (ObservationType != 0) hash ^= ObservationType.GetHashCode();
       hash ^= (int) observationDataCase_;
       if (_unknownFields != null) {
         hash ^= _unknownFields.GetHashCode();
       }
       compressedChannelMapping_.WriteTo(output, _repeated_compressedChannelMapping_codec);
       dimensionProperties_.WriteTo(output, _repeated_dimensionProperties_codec);
+      if (ObservationType != 0) {
+        output.WriteRawTag(56);
+        output.WriteEnum((int) ObservationType);
+      }
       if (_unknownFields != null) {
         _unknownFields.WriteTo(output);
       }
       }
       size += compressedChannelMapping_.CalculateSize(_repeated_compressedChannelMapping_codec);
       size += dimensionProperties_.CalculateSize(_repeated_dimensionProperties_codec);
+      if (ObservationType != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeEnumSize((int) ObservationType);
+      }
       if (_unknownFields != null) {
         size += _unknownFields.CalculateSize();
       }
       }
       compressedChannelMapping_.Add(other.compressedChannelMapping_);
       dimensionProperties_.Add(other.dimensionProperties_);
+      if (other.ObservationType != 0) {
+        ObservationType = other.ObservationType;
+      }
       switch (other.ObservationDataCase) {
         case ObservationDataOneofCase.CompressedData:
           CompressedData = other.CompressedData;
           case 50:
           case 48: {
             dimensionProperties_.AddEntriesFrom(input, _repeated_dimensionProperties_codec);
+            break;
+          }
+          case 56: {
+            observationType_ = (global::Unity.MLAgents.CommunicatorObjects.ObservationTypeProto) input.ReadEnum();
             break;
           }
         }

com.unity.ml-agents/Runtime/Inference/BarracudaModelParamLoader.cs

             for (var sensorIndex = 0; sensorIndex < sensorComponents.Length; sensorIndex++)
             {
                 var sensor = sensorComponents[sensorIndex];
-                if (!sensor.IsVisual())
+                if (sensor.GetObservationShape().Length == 3)
-                    continue;
+                    if (!tensorsNames.Contains(
+                        TensorNames.VisualObservationPlaceholderPrefix + visObsIndex))
+                    {
+                        failedModelChecks.Add(
+                            "The model does not contain a Visual Observation Placeholder Input " +
+                            $"for sensor component {visObsIndex} ({sensor.GetType().Name}).");
+                    }
+                    visObsIndex++;
-                if (!tensorsNames.Contains(
-                    TensorNames.VisualObservationPlaceholderPrefix + visObsIndex))
+                if (sensor.GetObservationShape().Length == 2)
-                    failedModelChecks.Add(
-                        "The model does not contain a Visual Observation Placeholder Input " +
-                        $"for sensor component {visObsIndex} ({sensor.GetType().Name}).");
+                    if (!tensorsNames.Contains(
+                        TensorNames.ObservationPlaceholderPrefix + sensorIndex))
+                    {
+                        failedModelChecks.Add(
+                            "The model does not contain an Observation Placeholder Input " +
+                            $"for sensor component {sensorIndex} ({sensor.GetType().Name}).");
+                    }
-                visObsIndex++;
             }
 
             var expectedVisualObs = model.GetNumVisualInputs();
         }
 
         /// <summary>
+        /// Checks that the shape of the rank 2 observation input placeholder is the same as the corresponding sensor.
+        /// </summary>
+        /// <param name="tensorProxy">The tensor that is expected by the model</param>
+        /// <param name="sensorComponent">The sensor that produces the visual observation.</param>
+        /// <returns>
+        /// If the Check failed, returns a string containing information about why the
+        /// check failed. If the check passed, returns null.
+        /// </returns>
+        static string CheckRankTwoObsShape(
+            TensorProxy tensorProxy, SensorComponent sensorComponent)
+        {
+            var shape = sensorComponent.GetObservationShape();
+            var dim1Bp = shape[0];
+            var dim2Bp = shape[1];
+            var dim1T = tensorProxy.Channels;
+            var dim2T = tensorProxy.Width;
+            if ((dim1Bp != dim1T) || (dim2Bp != dim2T))
+            {
+                return $"An Observation of the model does not match. " +
+                    $"Received TensorProxy of shape [?x{dim1Bp}x{dim2Bp}] but " +
+                    $"was expecting [?x{dim1T}x{dim2T}].";
+            }
+            return null;
+        }
+
+        /// <summary>
         /// Generates failed checks that correspond to inputs shapes incompatibilities between
         /// the model and the BrainParameters.
         /// </summary>
             for (var sensorIndex = 0; sensorIndex < sensorComponents.Length; sensorIndex++)
             {
                 var sensorComponent = sensorComponents[sensorIndex];
-                if (!sensorComponent.IsVisual())
+                if (sensorComponent.GetObservationShape().Length == 3)
+                {
+
+                    tensorTester[TensorNames.VisualObservationPlaceholderPrefix + visObsIndex] =
+                        (bp, tensor, scs, i) => CheckVisualObsShape(tensor, sensorComponent);
+                    visObsIndex++;
+                }
+                if (sensorComponent.GetObservationShape().Length == 2)
-                    continue;
+                    tensorTester[TensorNames.ObservationPlaceholderPrefix + sensorIndex] =
+                        (bp, tensor, scs, i) => CheckRankTwoObsShape(tensor, sensorComponent);
-                tensorTester[TensorNames.VisualObservationPlaceholderPrefix + visObsIndex] =
-                    (bp, tensor, scs, i) => CheckVisualObsShape(tensor, sensorComponent);
-                visObsIndex++;
             }
 
             // If the model expects an input but it is not in this list
             var totalVectorSensorSize = 0;
             foreach (var sensorComp in sensorComponents)
             {
-                if (sensorComp.IsVector())
+                if (sensorComp.GetObservationShape().Length == 1)
                 {
                     totalVectorSensorSize += sensorComp.GetObservationShape()[0];
                 }
                 var sensorSizes = "";
                 foreach (var sensorComp in sensorComponents)
                 {
-                    if (sensorComp.IsVector())
+                    if (sensorComp.GetObservationShape().Length == 1)
                     {
                         var vecSize = sensorComp.GetObservationShape()[0];
                         if (sensorSizes.Length == 0)

com.unity.ml-agents/Runtime/Inference/GeneratorImpl.cs

     }
 
     /// <summary>
-    /// Generates the Tensor corresponding to the VectorObservation input : Will be a two
-    /// dimensional float array of dimension [batchSize x vectorObservationSize].
-    /// It will use the Vector Observation data contained in the agentInfo to fill the data
-    /// of the tensor.
-    /// </summary>
-    internal class VectorObservationGenerator : TensorGenerator.IGenerator
-    {
-        readonly ITensorAllocator m_Allocator;
-        List<int> m_SensorIndices = new List<int>();
-        ObservationWriter m_ObservationWriter = new ObservationWriter();
-
-        public VectorObservationGenerator(ITensorAllocator allocator)
-        {
-            m_Allocator = allocator;
-        }
-
-        public void AddSensorIndex(int sensorIndex)
-        {
-            m_SensorIndices.Add(sensorIndex);
-        }
-
-        public void Generate(TensorProxy tensorProxy, int batchSize, IEnumerable<AgentInfoSensorsPair> infos)
-        {
-            TensorUtils.ResizeTensor(tensorProxy, batchSize, m_Allocator);
-            var vecObsSizeT = tensorProxy.shape[tensorProxy.shape.Length - 1];
-            var agentIndex = 0;
-            foreach (var info in infos)
-            {
-                if (info.agentInfo.done)
-                {
-                    // If the agent is done, we might have a stale reference to the sensors
-                    // e.g. a dependent object might have been disposed.
-                    // To avoid this, just fill observation with zeroes instead of calling sensor.Write.
-                    TensorUtils.FillTensorBatch(tensorProxy, agentIndex, 0.0f);
-                }
-                else
-                {
-                    var tensorOffset = 0;
-                    // Write each sensor consecutively to the tensor
-                    foreach (var sensorIndex in m_SensorIndices)
-                    {
-                        var sensor = info.sensors[sensorIndex];
-                        m_ObservationWriter.SetTarget(tensorProxy, agentIndex, tensorOffset);
-                        var numWritten = sensor.Write(m_ObservationWriter);
-                        tensorOffset += numWritten;
-                    }
-                    Debug.AssertFormat(
-                        tensorOffset == vecObsSizeT,
-                        "mismatch between vector observation size ({0}) and number of observations written ({1})",
-                        vecObsSizeT, tensorOffset
-                    );
-                }
-
-                agentIndex++;
-            }
-        }
-    }
-
-    /// <summary>
     /// Generates the Tensor corresponding to the Recurrent input : Will be a two
     /// dimensional float array of dimension [batchSize x memorySize].
     /// It will use the Memory data contained in the agentInfo to fill the data
     }
 
     /// <summary>
-    /// Generates the Tensor corresponding to the Visual Observation input : Will be a 4
-    /// dimensional float array of dimension [batchSize x width x height x numChannels].
-    /// It will use the Texture input data contained in the agentInfo to fill the data
+    /// Generates the Tensor corresponding to the Observation input : Will be a multi
+    /// dimensional float array.
+    /// It will use the Observation data contained in the sensors to fill the data
-    internal class VisualObservationInputGenerator : TensorGenerator.IGenerator
+    internal class ObservationGenerator : TensorGenerator.IGenerator
-        readonly int m_SensorIndex;
+        List<int> m_SensorIndices = new List<int>();
-        public VisualObservationInputGenerator(
-            int sensorIndex, ITensorAllocator allocator)
+        public ObservationGenerator(ITensorAllocator allocator)
-            m_SensorIndex = sensorIndex;
+        public void AddSensorIndex(int sensorIndex)
+        {
+            m_SensorIndices.Add(sensorIndex);
+        }
+
-            foreach (var infoSensorPair in infos)
+            foreach (var info in infos)
-                var sensor = infoSensorPair.sensors[m_SensorIndex];
-                if (infoSensorPair.agentInfo.done)
+                if (info.agentInfo.done)
                 {
                     // If the agent is done, we might have a stale reference to the sensors
                     // e.g. a dependent object might have been disposed.
                 else
                 {
-                    m_ObservationWriter.SetTarget(tensorProxy, agentIndex, 0);
-                    sensor.Write(m_ObservationWriter);
+                    var tensorOffset = 0;
+                    // Write each sensor consecutively to the tensor
+                    foreach (var sensorIndex in m_SensorIndices)
+                    {
+                        var sensor = info.sensors[sensorIndex];
+                        m_ObservationWriter.SetTarget(tensorProxy, agentIndex, tensorOffset);
+                        var numWritten = sensor.Write(m_ObservationWriter);
+                        tensorOffset += numWritten;
+                    }
                 }
                 agentIndex++;
             }

com.unity.ml-agents/Runtime/Inference/TensorGenerator.cs

         public void InitializeObservations(List<ISensor> sensors, ITensorAllocator allocator)
         {
             // Loop through the sensors on a representative agent.
-            // For vector observations, add the index to the (single) VectorObservationGenerator
-            // For visual observations, make a VisualObservationInputGenerator
+            // All vector observations use a shared ObservationGenerator since they are concatenated.
+            // All other observations use a unique ObservationInputGenerator
-            VectorObservationGenerator vecObsGen = null;
+            ObservationGenerator vecObsGen = null;
-                // TODO generalize - we currently only have vector or visual, but can't handle "2D" observations
-                var isVectorSensor = (shape.Length == 1);
-                if (isVectorSensor)
-                {
-                    if (vecObsGen == null)
-                    {
-                        vecObsGen = new VectorObservationGenerator(allocator);
-                    }
-
-                    vecObsGen.AddSensorIndex(sensorIndex);
-                }
-                else
+                var rank = shape.Length;
+                ObservationGenerator obsGen = null;
+                string obsGenName = null;
+                switch (rank)
-                    m_Dict[TensorNames.VisualObservationPlaceholderPrefix + visIndex] =
-                        new VisualObservationInputGenerator(sensorIndex, allocator);
-                    visIndex++;
+                    case 1:
+                        if (vecObsGen == null)
+                        {
+                            vecObsGen = new ObservationGenerator(allocator);
+                        }
+                        obsGen = vecObsGen;
+                        obsGenName = TensorNames.VectorObservationPlaceholder;
+                        break;
+                    case 2:
+                        // If the tensor is of rank 2, we use the index of the sensor
+                        // to create the name
+                        obsGen = new ObservationGenerator(allocator);
+                        obsGenName = TensorNames.ObservationPlaceholderPrefix + sensorIndex;
+                        break;
+                    case 3:
+                        // If the tensor is of rank 3, we use the "visual observation
+                        // index", which only counts the rank 3 sensors
+                        obsGen = new ObservationGenerator(allocator);
+                        obsGenName = TensorNames.VisualObservationPlaceholderPrefix + visIndex;
+                        visIndex++;
+                        break;
+                    default:
+                        throw new UnityAgentsException(
+                            $"Sensor {sensor.GetName()} have an invalid rank {rank}");
-            }
-
-            if (vecObsGen != null)
-            {
-                m_Dict[TensorNames.VectorObservationPlaceholder] = vecObsGen;
+                obsGen.AddSensorIndex(sensorIndex);
+                m_Dict[obsGenName] = obsGen;
             }
         }
 

com.unity.ml-agents/Runtime/Inference/TensorNames.cs

         public const string recurrentInPlaceholderH = "recurrent_in_h";
         public const string recurrentInPlaceholderC = "recurrent_in_c";
         public const string VisualObservationPlaceholderPrefix = "visual_observation_";
+        public const string ObservationPlaceholderPrefix = "obs_";
         public const string PreviousActionPlaceholder = "prev_action";
         public const string ActionMaskPlaceholder = "action_masks";
         public const string RandomNormalEpsilonPlaceholder = "epsilon";

com.unity.ml-agents/Runtime/Sensors/BufferSensor.cs

 
 namespace Unity.MLAgents.Sensors
 {
-    public class BufferSensor : ISensor, IDimensionPropertiesSensor
+    internal class BufferSensor : ISensor, IDimensionPropertiesSensor
     {
         private int m_MaxNumObs;
         private int m_ObsSize;

com.unity.ml-agents/Runtime/Sensors/BufferSensorComponent.cs

     /// A component for BufferSensor.
     /// </summary>
     [AddComponentMenu("ML Agents/Buffer Sensor", (int)MenuGroup.Sensors)]
-    public class BufferSensorComponent : SensorComponent
+    internal class BufferSensorComponent : SensorComponent
     {
         public int ObservableSize;
         public int MaxNumObservables;

com.unity.ml-agents/Runtime/Sensors/IDimensionPropertiesSensor.cs

     /// The Dimension property flags of the observations
     /// </summary>
     [System.Flags]
-    public enum DimensionProperty
+    internal enum DimensionProperty
     {
         /// <summary>
         /// No properties specified.
     /// <summary>
     /// Sensor interface for sensors with special dimension properties.
     /// </summary>
-    public interface IDimensionPropertiesSensor
+    internal interface IDimensionPropertiesSensor
     {
         /// <summary>
         /// Returns the array containing the properties of each dimensions of the

com.unity.ml-agents/Runtime/Sensors/SensorComponent.cs

 using UnityEngine;
+using System;
 
 namespace Unity.MLAgents.Sensors
 {
         /// Whether the observation is visual or not.
         /// </summary>
         /// <returns>True if the observation is visual, false otherwise.</returns>
+        [Obsolete("IsVisual is deprecated, please use GetObservationShape() instead.")]
         public virtual bool IsVisual()
         {
             var shape = GetObservationShape();
         /// Whether the observation is vector or not.
         /// </summary>
         /// <returns>True if the observation is vector, false otherwise.</returns>
+        [Obsolete("IsVisual is deprecated, please use GetObservationShape() instead.")]
         public virtual bool IsVector()
         {
             var shape = GetObservationShape();

com.unity.ml-agents/Runtime/StatsRecorder.cs

         /// To avoid conflicts when training with multiple concurrent environments, only
         /// stats from worker index 0 will be tracked.
         /// </summary>
-        MostRecent = 1
+        MostRecent = 1,
+
+        /// <summary>
+        /// Values within the summary period are summed up before reporting.
+        /// </summary>
+        Sum = 2
     }
 
     /// <summary>

com.unity.ml-agents/Tests/Editor/EditModeTestInternalBrainTensorGenerator.cs

             const int batchSize = 4;
             var agentInfos = GetFakeAgents(ObservableAttributeOptions.ExamineAll);
             var alloc = new TensorCachingAllocator();
-            var generator = new VectorObservationGenerator(alloc);
+            var generator = new ObservationGenerator(alloc);
             generator.AddSensorIndex(0); // ObservableAttribute (size 1)
             generator.AddSensorIndex(1); // TestSensor (size 0)
             generator.AddSensorIndex(2); // TestSensor (size 0)

com.unity.ml-agents/Tests/Editor/Sensor/CameraSensorComponentTest.cs

 
                     var expectedShape = new[] { height, width, grayscale ? 1 : 3 };
                     Assert.AreEqual(expectedShape, cameraComponent.GetObservationShape());
-                    Assert.IsTrue(cameraComponent.IsVisual());
-                    Assert.IsFalse(cameraComponent.IsVector());
 
                     var sensor = cameraComponent.CreateSensor();
                     Assert.AreEqual(expectedShape, sensor.GetObservationShape());

com.unity.ml-agents/Tests/Editor/Sensor/RenderTextureSensorComponentTests.cs

 
                     var expectedShape = new[] { height, width, grayscale ? 1 : 3 };
                     Assert.AreEqual(expectedShape, renderTexComponent.GetObservationShape());
-                    Assert.IsTrue(renderTexComponent.IsVisual());
-                    Assert.IsFalse(renderTexComponent.IsVector());
 
                     var sensor = renderTexComponent.CreateSensor();
                     Assert.AreEqual(expectedShape, sensor.GetObservationShape());

docs/Background-Unity.md

 If you are not familiar with the [Unity Engine](https://unity3d.com/unity), we
 highly recommend the [Unity Manual](https://docs.unity3d.com/Manual/index.html)
 and [Tutorials page](https://unity3d.com/learn/tutorials). The
-[Roll-a-ball tutorial](https://unity3d.com/learn/tutorials/s/roll-ball-tutorial)
+[Roll-a-ball tutorial](https://learn.unity.com/project/roll-a-ball)
 is a fantastic resource to learn all the basic concepts of Unity to get started
 with the ML-Agents Toolkit:
 

docs/FAQ.md

 
 ## Installation problems
 
-### Tensorflow dependency
-
-ML Agents requires TensorFlow; if you don't already have it installed, `pip`
-will try to install it when you install the ml-agents package.
-
-If you see a message like this
-
-```console
-ERROR: Could not find a version that satisfies the requirement tensorflow<2.0,>=1.7 (from mlagents) (from versions: none)
-ERROR: No matching distribution found for tensorflow<2.0,>=1.7 (from mlagents)
-```
-
-it means that there is no version of TensorFlow for your python environment.
-Some known potential causes are:
-
-- You're using 32-bit python instead of 64-bit. See the answer
-  [here](https://stackoverflow.com/a/1405971/224264) for how to tell which you
-  have installed.
-- You have the `tensorflow-gpu` package installed. This is equivalent to
-  `tensorflow`, however `pip` doesn't recognize this. The best way to resolve
-  this is to update to `tensorflow==1.15.0` which provides GPU support in the
-  same package (see the
-  [release notes](https://github.com/tensorflow/tensorflow/issues/33374) for
-  more details.)
-- You're on another architecture (e.g. ARM) which requires vendor provided
-  packages.
-
-In all of these cases, the issue is a pip/python environment setup issue. Please
-search the tensorflow github issues for similar problems and solutions before
-creating a new issue.
-
-#### Visual C++ Dependency (Windows Users)
-When running `mlagents-learn`, if you see a stack trace with a message like this:
-
-```console
-ImportError: DLL load failed: The specified module could not be found.
-```
-
-then either of the required DLLs, `msvcp140.dll` (old) or `msvcp140_1.dll` (new), are missing on your machine. The `import tensorflow` command will print this warning message.
-
-To solve it, download and install (with a reboot) the install [Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019](https://support.microsoft.com/en-my/help/2977003/the-latest-supported-visual-c-downloads).
-
-For more details, please see the [TensorFlow 2.1.0 release notes](https://github.com/tensorflow/tensorflow/releases/tag/v2.1.0)
-and the [TensorFlow github issue](https://github.com/tensorflow/tensorflow/issues/22794#issuecomment-573297027).
-
 ## Environment Permission Error
 
 If you directly import your Unity environment without building it in the editor,

docs/Learning-Environment-Design-Agents.md

     - [Visual Observation Summary & Best Practices](#visual-observation-summary--best-practices)
   - [Raycast Observations](#raycast-observations)
     - [RayCast Observation Summary & Best Practices](#raycast-observation-summary--best-practices)
-- [Actions](#actions)
+- [Actions and Actuators](#actions-and-actuators)
   - [Continuous Actions](#continuous-actions)
   - [Discrete Actions](#discrete-actions)
     - [Masking Discrete Actions](#masking-discrete-actions)
 - Use as few rays and tags as necessary to solve the problem in order to improve
   learning stability and agent performance.
 
-## Actions
+## Actions and Actuators
-action is passed to the Agent as the `ActionBuffers` parameter when the Academy invokes the
-agent's `OnActionReceived()` function. There are two types of actions that an Agent can use:
- **Continuous** and **Discrete**.
+action is passed to the an `IActionReceiver` (either an `Agent` or an `IActuator`)
+as the `ActionBuffers` parameter when the Academy invokes the
+`IActionReciever.OnActionReceived()` function.
+There are two types of actions supported: **Continuous** and **Discrete**.
 
 Neither the Policy nor the training algorithm know anything about what the
 action values themselves mean. The training algorithm simply tries different
   branches.
 - You cannot mask all the actions of a branch.
 - You cannot mask actions in continuous control.
+
+
+### IActuator interface and ActuatorComponents
+The Actuator API allows users to abstract behavior out of Agents and in to
+components (similar to the ISensor API).  The `IActuator` interface and `Agent`
+class both implement the `IActionReceiver` interface to allow for backward compatibility
+with the current `Agent.OnActionReceived` and `Agent.CollectDiscreteActionMasks` APIs.
+This means you will not have to change your code until you decide to use the `IActuator` API.
+
+Like the `ISensor` interface, the `IActuator` interface is intended for advanced users.
+
+The `ActuatorComponent` abstract class is used to create the actual `IActuator` at
+runtime. It must be attached to the same `GameObject` as the `Agent`, or to a
+child `GameObject`.  Actuators and all of their data structures are initialized
+during `Agent.Initialize`.  This was done to prevent an unexpected allocations at runtime.
+
+You can find an example of an `IActuator` implementation in the `Basic` example scene.
+**NOTE**: you do not need to adjust the Actions in the Agent's
+`Behavior Parameters` when using an `IActuator` and `ActuatorComponents`.
+
+Internally, `Agent.OnActionReceived` uses an `IActuator` to send actions to the Agent,
+although this is mostly abstracted from the user.
+
 
 ### Actions Summary & Best Practices
 

docs/Python-API.md

 
 A `BehaviorSpec` has the following fields :
 
-- `sensor_specs` is a List of `SensorSpec` objects : Each `SensorSpec`
+- `observation_specs` is a List of `ObservationSpec` objects : Each `ObservationSpec`
-  data should be processed in the corresponding dimension. Note that the `SensorSpec`
-  have the same ordering as the ordering of observations in the DecisionSteps,
-  DecisionStep, TerminalSteps and TerminalStep.
+  data should be processed in the corresponding dimension. `observation_type` is an enum
+  corresponding to what type of observation is generating the data (i.e., default, goal,
+  etc). Note that the `ObservationSpec` have the same ordering as the ordering of observations
+  in the DecisionSteps, DecisionStep, TerminalSteps and TerminalStep.
 - `action_spec` is an `ActionSpec` namedtuple that defines the number and types
   of actions for the Agent.
 

gym-unity/gym_unity/envs/__init__.py

 
     def _get_n_vis_obs(self) -> int:
         result = 0
-        for sen_spec in self.group_spec.sensor_specs:
-            if len(sen_spec.shape) == 3:
+        for obs_spec in self.group_spec.observation_specs:
+            if len(obs_spec.shape) == 3:
-        for sen_spec in self.group_spec.sensor_specs:
-            if len(sen_spec.shape) == 3:
-                result.append(sen_spec.shape)
+        for obs_spec in self.group_spec.observation_specs:
+            if len(obs_spec.shape) == 3:
+                result.append(obs_spec.shape)
         return result
 
     def _get_vis_obs_list(
 
     def _get_vec_obs_size(self) -> int:
         result = 0
-        for sen_spec in self.group_spec.sensor_specs:
-            if len(sen_spec.shape) == 1:
-                result += sen_spec.shape[0]
+        for obs_spec in self.group_spec.observation_specs:
+            if len(obs_spec.shape) == 1:
+                result += obs_spec.shape[0]
         return result
 
     def render(self, mode="rgb_array"):

gym-unity/gym_unity/tests/test_gym.py

     TerminalSteps,
     BehaviorMapping,
 )
-from mlagents.trainers.tests.dummy_config import create_sensor_specs_with_shapes
+from mlagents.trainers.tests.dummy_config import create_observation_specs_with_shapes
 
 
 def test_gym_wrapper():
     obs_shapes = [(vector_observation_space_size,)]
     for _ in range(number_visual_observations):
         obs_shapes += [(8, 8, 3)]
-    sen_spec = create_sensor_specs_with_shapes(obs_shapes)
-    return BehaviorSpec(sen_spec, action_spec)
+    obs_spec = create_observation_specs_with_shapes(obs_shapes)
+    return BehaviorSpec(obs_spec, action_spec)
 
 
 def create_mock_vector_steps(specs, num_agents=1, number_visual_observations=0):

ml-agents-envs/mlagents_envs/base_env.py

     Any,
     Mapping as MappingType,
 )
-from enum import IntFlag
+from enum import IntFlag, Enum
 import numpy as np
 
 from mlagents_envs.exception import UnityActionException
         :param spec: The BehaviorSpec for the DecisionSteps
         """
         obs: List[np.ndarray] = []
-        for sen_spec in spec.sensor_specs:
+        for sen_spec in spec.observation_specs:
             obs += [np.zeros((0,) + sen_spec.shape, dtype=np.float32)]
         return DecisionSteps(
             obs=obs,
         :param spec: The BehaviorSpec for the TerminalSteps
         """
         obs: List[np.ndarray] = []
-        for sen_spec in spec.sensor_specs:
+        for sen_spec in spec.observation_specs:
             obs += [np.zeros((0,) + sen_spec.shape, dtype=np.float32)]
         return TerminalSteps(
             obs=obs,
     VARIABLE_SIZE = 4
 
 
-class SensorSpec(NamedTuple):
+class ObservationType(Enum):
+    """
+    An Enum which defines the type of information carried in the observation
+    of the agent.
+    """
+
+    # Observation information is generic.
+    DEFAULT = 0
+    # Observation contains goal information for current task.
+    GOAL = 1
+    # Observation contains reward information for current task.
+    REWARD = 2
+    # Observation contains a message from another agent.
+    MESSAGE = 3
+
+
+class ObservationSpec(NamedTuple):
     """
     A NamedTuple containing information about the observation of Agents.
     - shape is a Tuple of int : It corresponds to the shape of
+    - observation_type is an enum of ObservationType.
+    observation_type: ObservationType
 
 
 class BehaviorSpec(NamedTuple):
-    - sensor_specs is a List of SensorSpec NamedTuple containing
+    - observation_specs is a List of ObservationSpec NamedTuple containing
     information about the information of the Agent's observations such as their shapes.
     The order of the SensorSpec is the same as the order of the observations of an
     agent.
-    sensor_specs: List[SensorSpec]
+    observation_specs: List[ObservationSpec]
     action_spec: ActionSpec
 
 

ml-agents-envs/mlagents_envs/communicator_objects/observation_pb2.py

   name='mlagents_envs/communicator_objects/observation.proto',
   package='communicator_objects',
   syntax='proto3',
-  serialized_pb=_b('\n4mlagents_envs/communicator_objects/observation.proto\x12\x14\x63ommunicator_objects\"\xbb\x02\n\x10ObservationProto\x12\r\n\x05shape\x18\x01 \x03(\x05\x12\x44\n\x10\x63ompression_type\x18\x02 \x01(\x0e\x32*.communicator_objects.CompressionTypeProto\x12\x19\n\x0f\x63ompressed_data\x18\x03 \x01(\x0cH\x00\x12\x46\n\nfloat_data\x18\x04 \x01(\x0b\x32\x30.communicator_objects.ObservationProto.FloatDataH\x00\x12\"\n\x1a\x63ompressed_channel_mapping\x18\x05 \x03(\x05\x12\x1c\n\x14\x64imension_properties\x18\x06 \x03(\x05\x1a\x19\n\tFloatData\x12\x0c\n\x04\x64\x61ta\x18\x01 \x03(\x02\x42\x12\n\x10observation_data*)\n\x14\x43ompressionTypeProto\x12\x08\n\x04NONE\x10\x00\x12\x07\n\x03PNG\x10\x01\x42%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
+  serialized_pb=_b('\n4mlagents_envs/communicator_objects/observation.proto\x12\x14\x63ommunicator_objects\"\x81\x03\n\x10ObservationProto\x12\r\n\x05shape\x18\x01 \x03(\x05\x12\x44\n\x10\x63ompression_type\x18\x02 \x01(\x0e\x32*.communicator_objects.CompressionTypeProto\x12\x19\n\x0f\x63ompressed_data\x18\x03 \x01(\x0cH\x00\x12\x46\n\nfloat_data\x18\x04 \x01(\x0b\x32\x30.communicator_objects.ObservationProto.FloatDataH\x00\x12\"\n\x1a\x63ompressed_channel_mapping\x18\x05 \x03(\x05\x12\x1c\n\x14\x64imension_properties\x18\x06 \x03(\x05\x12\x44\n\x10observation_type\x18\x07 \x01(\x0e\x32*.communicator_objects.ObservationTypeProto\x1a\x19\n\tFloatData\x12\x0c\n\x04\x64\x61ta\x18\x01 \x03(\x02\x42\x12\n\x10observation_data*)\n\x14\x43ompressionTypeProto\x12\x08\n\x04NONE\x10\x00\x12\x07\n\x03PNG\x10\x01*F\n\x14ObservationTypeProto\x12\x0b\n\x07\x44\x45\x46\x41ULT\x10\x00\x12\x08\n\x04GOAL\x10\x01\x12\n\n\x06REWARD\x10\x02\x12\x0b\n\x07MESSAGE\x10\x03\x42%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
 )
 
 _COMPRESSIONTYPEPROTO = _descriptor.EnumDescriptor(
   ],
   containing_type=None,
   options=None,
-  serialized_start=396,
-  serialized_end=437,
+  serialized_start=466,
+  serialized_end=507,
+_OBSERVATIONTYPEPROTO = _descriptor.EnumDescriptor(
+  name='ObservationTypeProto',
+  full_name='communicator_objects.ObservationTypeProto',
+  filename=None,
+  file=DESCRIPTOR,
+  values=[
+    _descriptor.EnumValueDescriptor(
+      name='DEFAULT', index=0, number=0,
+      options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='GOAL', index=1, number=1,
+      options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='REWARD', index=2, number=2,
+      options=None,
+      type=None),
+    _descriptor.EnumValueDescriptor(
+      name='MESSAGE', index=3, number=3,
+      options=None,
+      type=None),
+  ],
+  containing_type=None,
+  options=None,
+  serialized_start=509,
+  serialized_end=579,
+)
+_sym_db.RegisterEnumDescriptor(_OBSERVATIONTYPEPROTO)
+
+ObservationTypeProto = enum_type_wrapper.EnumTypeWrapper(_OBSERVATIONTYPEPROTO)
+DEFAULT = 0
+GOAL = 1
+REWARD = 2
+MESSAGE = 3