Revert "Merge branch 'main' into develop-superpush-branch-cleanup"

This reverts commit 5bea802525381f931a5e0f8b8778fe27a12f03af, reversing
changes made to cee3524e85161e13689d95f66bc6bff994d2cdfd.

.github/workflows/pytest.yml

         TEST_ENFORCE_BUFFER_KEY_TYPES: 1
     strategy:
       matrix:
-        python-version: [3.6.x, 3.7.x, 3.8.x, 3.9.x]
-        include:
-          - python-version: 3.6.x
-            pip_constraints: test_constraints_min_version.txt
-          - python-version: 3.7.x
-            pip_constraints: test_constraints_mid_version.txt
-          - python-version: 3.8.x
-            pip_constraints: test_constraints_mid_version.txt
-          - python-version: 3.9.x
-            pip_constraints: test_constraints_max_version.txt
+        python-version: [3.6.x, 3.7.x, 3.8.x]
     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 }}-
       run: |
         python -m pip install --upgrade pip
         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-plugin-examples -c ${{ matrix.pip_constraints }}
+        python -m pip install --progress-bar=off -e ./ml-agents-envs
+        python -m pip install --progress-bar=off -e ./ml-agents
+        python -m pip install --progress-bar=off -r test_requirements.txt
+        python -m pip install --progress-bar=off -e ./gym-unity
+        python -m pip install --progress-bar=off -e ./ml-agents-plugin-examples
     - name: Save python dependencies
       run: |
         pip freeze > pip_versions-${{ matrix.python-version }}.txt

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

     image: package-ci/ubuntu:stable
     flavor: b1.small
   commands:
-    - |
-      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 2018.4 -c editor --wait --fast
-      ./.Editor/Unity -projectPath Project -batchMode -executeMethod Unity.MLAgents.SampleExporter.ExportCuratedSamples -logFile -
-      npm install upm-ci-utils@stable -g --registry https://artifactory.prd.cds.internal.unity3d.com/artifactory/api/npm/upm-npm
-      upm-ci project pack --project-path Project
+    - npm install upm-ci-utils@stable -g --registry https://artifactory.prd.cds.internal.unity3d.com/artifactory/api/npm/upm-npm
+    - upm-ci project pack --project-path Project
   artifacts:
     packages:
       paths:

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

       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-Match3
+      python ml-agents/tests/yamato/scripts/run_llapi.py --env=artifacts/testPlayer-Bouncer
   dependencies:
     - .yamato/standalone-build-test.yml#test_linux_standalone_{{ editor.version }}
   triggers:

.yamato/standalone-build-test.yml

     - 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/Match3/Scenes/Match3.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

DevProject/Packages/packages-lock.json

       "url": "https://artifactory.prd.cds.internal.unity3d.com/artifactory/api/npm/upm-candidates"
     },
     "com.unity.barracuda": {
-      "version": "1.3.1-preview",
+      "version": "1.3.0-preview",
       "depth": 1,
       "source": "registry",
       "dependencies": {
       "depth": 0,
       "source": "local",
       "dependencies": {
-        "com.unity.barracuda": "1.3.1-preview",
+        "com.unity.barracuda": "1.3.0-preview",
         "com.unity.modules.imageconversion": "1.0.0",
         "com.unity.modules.jsonserialize": "1.0.0",
         "com.unity.modules.physics": "1.0.0",
       "depth": 0,
       "source": "local",
       "dependencies": {
-        "com.unity.ml-agents": "1.8.0-preview"
+        "com.unity.ml-agents": "1.7.2-preview"
       }
     },
     "com.unity.multiplayer-hlapi": {

Project/ProjectSettings/GraphicsSettings.asset

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-  - {fileID: 16001, guid: 0000000000000000f000000000000000, type: 0}
   - {fileID: 17000, guid: 0000000000000000f000000000000000, type: 0}
   m_PreloadedShaders: []
   m_SpritesDefaultMaterial: {fileID: 10754, guid: 0000000000000000f000000000000000,

Project/ProjectSettings/TagManager.asset

   - purpleAgent
   - purpleGoal
   - tile
+  - zombie
+  - blockLarge
+  - blockVeryLarge
+  - blockSmall
+  - door
+  - switch
+  - key
+  - lock
   layers:
   - Default
   - TransparentFX

Project/Assets/ML-Agents/Examples/Crawler/Demos/ExpCrawlerDynVS.demo.meta

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   fileIDToRecycleName:
-    11400002: Assets/ML-Agents/Examples/Crawler/Demos/ExpertCrawler.demo
+    11400002: Assets/ML-Agents/Examples/Crawler/Demos/ExpCrawlerDynVS.demo
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   userData: ' (Unity.MLAgents.Demonstrations.DemonstrationSummary)'
   assetBundleName: 

Project/Assets/ML-Agents/Examples/Crawler/Prefabs/CrawlerBase.prefab

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     hasUpgradedBrainParametersWithActionSpec: 1
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+  m_BehaviorName: 
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   hasUpgradedFromAgentParameters: 1
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Project/Assets/ML-Agents/Examples/Crawler/Scenes/CrawlerDynamicVariableSpeed.unity

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

 [RequireComponent(typeof(JointDriveController))] // Required to set joint forces
 public class CrawlerAgent : Agent
 {
+    //The type of crawler behavior we want to use.
+    //This setting will determine how the agent is set up during initialization.
+    public enum CrawlerAgentBehaviorType
+    {
+        CrawlerDynamic,
+        CrawlerDynamicVariableSpeed,
+        CrawlerStatic,
+        CrawlerStaticVariableSpeed,
+        CrawlerDynamicVariableSpeedCustomTarget
+    }
+
+    [Tooltip(
+        "VariableSpeed - The agent will sample random speed magnitudes while training.\n" +
+        "Dynamic - The agent will run towards a target that changes position.\n" +
+        "Static - The agent will run towards a static target. "
+    )]
+    public CrawlerAgentBehaviorType typeOfCrawler;
+
+    //Crawler Brains
+    //A different brain will be used depending on the CrawlerAgentBehaviorType selected
+    [Header("NN Models")] public NNModel crawlerDyModel;
+    public NNModel crawlerDyVSModel;
+    public NNModel crawlerStModel;
+    public NNModel crawlerStVSModel;
 
     [Header("Walk Speed")]
     [Range(0.1f, m_maxWalkingSpeed)]
         set { m_TargetWalkingSpeed = Mathf.Clamp(value, .1f, m_maxWalkingSpeed); }
     }
 
+    //Should the agent sample a new goal velocity each episode?
+    //If true, TargetWalkingSpeed will be randomly set between 0.1 and m_maxWalkingSpeed in OnEpisodeBegin()
+    //If false, the goal velocity will be m_maxWalkingSpeed
+    private bool m_RandomizeWalkSpeedEachEpisode;
+
-    [Header("Target To Walk Towards")]
-    public Transform TargetPrefab; //Target prefab to use in Dynamic envs
-    private Transform m_Target; //Target the agent will walk towards during training.
+    [Header("Target To Walk Towards")] public Transform dynamicTargetPrefab; //Target prefab to use in Dynamic envs
+    public Transform staticTargetPrefab; //Target prefab to use in Static envs
+    public Transform m_Target; //Target the agent will walk towards during training.
 
     [Header("Body Parts")] [Space(10)] public Transform body;
     public Transform leg0Upper;
 
     public override void Initialize()
     {
-        SpawnTarget(TargetPrefab, transform.position); //spawn target
+        SetAgentType();
 
         m_OrientationCube = GetComponentInChildren<OrientationCubeController>();
         m_DirectionIndicator = GetComponentInChildren<DirectionIndicator>();
     }
 
     /// <summary>
+    /// Set up the agent based on the typeOfCrawler
+    /// </summary>
+    void SetAgentType()
+    {
+        var behaviorParams = GetComponent<Unity.MLAgents.Policies.BehaviorParameters>();
+        switch (typeOfCrawler)
+        {
+            case CrawlerAgentBehaviorType.CrawlerDynamicVariableSpeedCustomTarget:
+                {
+                    behaviorParams.BehaviorName = "CrawlerDynamicVariableSpeed"; //set behavior name
+                    if (crawlerDyVSModel)
+                        behaviorParams.Model = crawlerDyVSModel; //assign the model
+                    m_RandomizeWalkSpeedEachEpisode = true; //randomize m_TargetWalkingSpeed during training
+                    break;
+                }
+            case CrawlerAgentBehaviorType.CrawlerDynamic:
+                {
+                    behaviorParams.BehaviorName = "CrawlerDynamic"; //set behavior name
+                    if (crawlerDyModel)
+                        behaviorParams.Model = crawlerDyModel; //assign the model
+                    m_RandomizeWalkSpeedEachEpisode = false; //do not randomize m_TargetWalkingSpeed during training
+                    SpawnTarget(dynamicTargetPrefab, transform.position); //spawn target
+                    break;
+                }
+            case CrawlerAgentBehaviorType.CrawlerDynamicVariableSpeed:
+                {
+                    behaviorParams.BehaviorName = "CrawlerDynamicVariableSpeed"; //set behavior name
+                    if (crawlerDyVSModel)
+                        behaviorParams.Model = crawlerDyVSModel; //assign the model
+                    m_RandomizeWalkSpeedEachEpisode = true; //randomize m_TargetWalkingSpeed during training
+                    SpawnTarget(dynamicTargetPrefab, transform.position); //spawn target
+                    break;
+                }
+            case CrawlerAgentBehaviorType.CrawlerStatic:
+                {
+                    behaviorParams.BehaviorName = "CrawlerStatic"; //set behavior name
+                    if (crawlerStModel)
+                        behaviorParams.Model = crawlerStModel; //assign the model
+                    m_RandomizeWalkSpeedEachEpisode = false; //do not randomize m_TargetWalkingSpeed during training
+                    SpawnTarget(staticTargetPrefab, transform.TransformPoint(new Vector3(0, 0, 1000))); //spawn target
+                    break;
+                }
+            case CrawlerAgentBehaviorType.CrawlerStaticVariableSpeed:
+                {
+                    behaviorParams.BehaviorName = "CrawlerStaticVariableSpeed"; //set behavior name
+                    if (crawlerStVSModel)
+                        behaviorParams.Model = crawlerStVSModel; //assign the model
+                    m_RandomizeWalkSpeedEachEpisode = true; //randomize m_TargetWalkingSpeed during training
+                    SpawnTarget(staticTargetPrefab, transform.TransformPoint(new Vector3(0, 0, 1000))); //spawn target
+                    break;
+                }
+        }
+    }
+
+    /// <summary>
     /// Loop over body parts and reset them to initial conditions.
     /// </summary>
     public override void OnEpisodeBegin()
         UpdateOrientationObjects();
 
         //Set our goal walking speed
-        TargetWalkingSpeed = Random.Range(0.1f, m_maxWalkingSpeed);
+        TargetWalkingSpeed =
+            m_RandomizeWalkSpeedEachEpisode ? Random.Range(0.1f, m_maxWalkingSpeed) : TargetWalkingSpeed;
     }
 
     /// <summary>

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

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Project/Assets/ML-Agents/Examples/Sorter/Scripts/SorterAgent.cs

 
     int m_NumberOfTilesToSpawn;
     int m_MaxNumberOfTiles;
+    PushBlockSettings m_PushBlockSettings;
     Rigidbody m_AgentRb;
 
     // The BufferSensorComponent is the Sensor that allows the Agent to observe
         m_MaxNumberOfTiles = k_HighestTileValue;
         m_ResetParams = Academy.Instance.EnvironmentParameters;
         m_BufferSensor = GetComponent<BufferSensorComponent>();
+        m_PushBlockSettings = FindObjectOfType<PushBlockSettings>();
         m_AgentRb = GetComponent<Rigidbody>();
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         transform.Rotate(rotateDir, Time.deltaTime * 200f);
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Project/Assets/ML-Agents/Examples/Worm/Scripts/WormAgent.cs

 [RequireComponent(typeof(JointDriveController))] // Required to set joint forces
 public class WormAgent : Agent
 {
+    //The type of agent behavior we want to use.
+    //This setting will determine how the agent is set up during initialization.
+    public enum WormAgentBehaviorType
+    {
+        WormDynamic,
+        WormStatic
+    }
+
+    [Tooltip(
+        "Dynamic - The agent will run towards a target that changes position.\n\n" +
+        "Static - The agent will run towards a static target. "
+    )]
+    public WormAgentBehaviorType typeOfWorm;
+
-    [Header("Target Prefabs")] public Transform TargetPrefab; //Target prefab to use in Dynamic envs
+    //Brains
+    //A different brain will be used depending on the CrawlerAgentBehaviorType selected
+    [Header("NN Models")] public NNModel wormDyModel;
+    public NNModel wormStModel;
+
+    [Header("Target Prefabs")] public Transform dynamicTargetPrefab; //Target prefab to use in Dynamic envs
+    public Transform staticTargetPrefab; //Target prefab to use in Static envs
     private Transform m_Target; //Target the agent will walk towards during training.
 
     [Header("Body Parts")] public Transform bodySegment0;
 
     public override void Initialize()
     {
-        SpawnTarget(TargetPrefab, transform.position); //spawn target
+        SetAgentType();
+
 
         m_StartingPos = bodySegment0.position;
         m_OrientationCube = GetComponentInChildren<OrientationCubeController>();
     void SpawnTarget(Transform prefab, Vector3 pos)
     {
         m_Target = Instantiate(prefab, pos, Quaternion.identity, transform);
+    }
+
+    /// <summary>
+    /// Set up the agent based on the type
+    /// </summary>
+    void SetAgentType()
+    {
+        var behaviorParams = GetComponent<Unity.MLAgents.Policies.BehaviorParameters>();
+        switch (typeOfWorm)
+        {
+            case WormAgentBehaviorType.WormDynamic:
+                {
+                    behaviorParams.BehaviorName = "WormDynamic"; //set behavior name
+                    if (wormDyModel)
+                        behaviorParams.Model = wormDyModel; //assign the brain
+                    SpawnTarget(dynamicTargetPrefab, transform.position); //spawn target
+                    break;
+                }
+            case WormAgentBehaviorType.WormStatic:
+                {
+                    behaviorParams.BehaviorName = "WormStatic"; //set behavior name
+                    if (wormStModel)
+                        behaviorParams.Model = wormStModel; //assign the brain
+                    SpawnTarget(staticTargetPrefab, transform.TransformPoint(new Vector3(0, 0, 1000))); //spawn target
+                    break;
+                }
+        }
     }
 
     /// <summary>

Project/Packages/manifest.json

   "dependencies": {
     "com.unity.ads": "2.0.8",
     "com.unity.analytics": "3.2.3",
+    "com.unity.barracuda": "1.2.1-preview",
     "com.unity.collab-proxy": "1.2.15",
     "com.unity.ml-agents": "file:../../com.unity.ml-agents",
     "com.unity.ml-agents.extensions": "file:../../com.unity.ml-agents.extensions",
     "com.unity.modules.video": "1.0.0",
     "com.unity.modules.vr": "1.0.0",
     "com.unity.modules.wind": "1.0.0",
-    "com.unity.modules.xr": "1.0.0",
-    "com.unity.nuget.newtonsoft-json": "2.0.0"
+    "com.unity.modules.xr": "1.0.0"
   },
   "testables": [
     "com.unity.ml-agents",

Project/Project.sln.DotSettings

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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_14_docs/docs/)
+[![docs badge](https://img.shields.io/badge/docs-reference-blue.svg)](https://github.com/Unity-Technologies/ml-agents/tree/release_13_docs/docs/)
 
 [![license badge](https://img.shields.io/badge/license-Apache--2.0-green.svg)](LICENSE)
 
 ## Releases & Documentation
 
 
-**Our latest, stable release is `Release 14`. Click
-[here](https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/docs/Readme.md)
+**Our latest, stable release is `Release 13`. Click
+[here](https://github.com/Unity-Technologies/ml-agents/tree/release_13_docs/docs/Readme.md)
 to get started with the latest release of ML-Agents.**
 
 The table below lists all our releases, including our `main` branch which is
 | **Version** | **Release Date** | **Source** | **Documentation** | **Download** | **Python Package** | **Unity Package** |
 |:-------:|:------:|:-------------:|:-------:|:------------:|:------------:|:------------:|
 | **main (unstable)** | -- | [source](https://github.com/Unity-Technologies/ml-agents/tree/main) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/main/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/main.zip) | -- | -- |
-| **Release 14** | **March 5, 2021** | **[source](https://github.com/Unity-Technologies/ml-agents/tree/release_14)** | **[docs](https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/docs/Readme.md)** | **[download](https://github.com/Unity-Technologies/ml-agents/archive/release_14.zip)** | **[0.24.1](https://pypi.org/project/mlagents/0.24.1/)** | **[1.8.1](https://docs.unity3d.com/Packages/com.unity.ml-agents@1.8/manual/index.html)** |
-| **Verified Package 1.0.7** | **March 8, 2021** | **[source](https://github.com/Unity-Technologies/ml-agents/tree/com.unity.ml-agents_1.0.7)** | **[docs](https://github.com/Unity-Technologies/ml-agents/blob/release_2_verified_docs/docs/Readme.md)** | **[download](https://github.com/Unity-Technologies/ml-agents/archive/com.unity.ml-agents_1.0.7.zip)** | **[0.16.1](https://pypi.org/project/mlagents/0.16.1/)** | **[1.0.7](https://docs.unity3d.com/Packages/com.unity.ml-agents@1.0/manual/index.html)** |
-| **Verified Package 1.0.6** | November 16, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/com.unity.ml-agents_1.0.6) | [docs](https://github.com/Unity-Technologies/ml-agents/blob/release_2_verified_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/com.unity.ml-agents_1.0.6.zip) | [0.16.1](https://pypi.org/project/mlagents/0.16.1/) | [1.0.6](https://docs.unity3d.com/Packages/com.unity.ml-agents@1.0/manual/index.html) |
+| **Verified Package 1.0.6** | **November 16, 2020** | **[source](https://github.com/Unity-Technologies/ml-agents/tree/com.unity.ml-agents_1.0.6)** | **[docs](https://github.com/Unity-Technologies/ml-agents/blob/release_2_verified_docs/docs/Readme.md)** | **[download](https://github.com/Unity-Technologies/ml-agents/archive/com.unity.ml-agents_1.0.6.zip)** | **[0.16.1](https://pypi.org/project/mlagents/0.16.1/)** | **[1.0.6](https://docs.unity3d.com/Packages/com.unity.ml-agents@1.0/manual/index.html)** |
+| **Verified Package 1.0.5** | September 23, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/com.unity.ml-agents_1.0.5) | [docs](https://github.com/Unity-Technologies/ml-agents/blob/release_2_verified_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/com.unity.ml-agents_1.0.5.zip) | [0.16.1](https://pypi.org/project/mlagents/0.16.1/) | [1.0.5](https://docs.unity3d.com/Packages/com.unity.ml-agents@1.0/manual/index.html) |
 
 If you are a researcher interested in a discussion of Unity as an AI platform,
 see a pre-print of our

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_14_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/blob/release_13_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_14_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_13_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_14_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_14_docs/docs/Installation.md#advanced-local-installation-for-development-1)
+[Clone the repository](https://github.com/Unity-Technologies/ml-agents/tree/release_13_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_13_docs/docs/Installation.md#advanced-local-installation-for-development-1)
-![Package Manager git URL](https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/images/unity_package_manager_git_url.png)
-
+![Package Manager git URL](https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/images/unity_package_manager_git_url.png)
-git+https://github.com/Unity-Technologies/ml-agents.git?path=com.unity.ml-agents.extensions#release_14
+ git+https://github.com/Unity-Technologies/ml-agents.git?path=com.unity.ml-agents.extensions
-"com.unity.ml-agents.extensions": "git+https://github.com/Unity-Technologies/ml-agents.git?path=com.unity.ml-agents.extensions#release_14",
+"com.unity.ml-agents.extensions": "git+https://github.com/Unity-Technologies/ml-agents.git?path=com.unity.ml-agents.extensions",
-See [Git dependencies](https://docs.unity3d.com/Manual/upm-git.html#subfolder) for more information. Note that this
-may take several minutes to resolve the packages the first time that you add it.
+See [Git dependencies](https://docs.unity3d.com/Manual/upm-git.html#subfolder) for more information.
 
 
 ## Requirements
     - No way to customize the action space of the `InputActuatorComponent`
 
 ## Need Help?
-The main [README](https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/README.md) contains links for contacting the team or getting support.
+The main [README](https://github.com/Unity-Technologies/ml-agents/tree/release_13_docs/README.md) contains links for contacting the team or getting support.

com.unity.ml-agents.extensions/Runtime/Input/Adaptors/ButtonInputActionAdaptor.cs

 using Unity.MLAgents.Actuators;
 using UnityEngine;
 using UnityEngine.InputSystem;
-using UnityEngine.InputSystem.Controls;
 using UnityEngine.InputSystem.LowLevel;
 
 namespace Unity.MLAgents.Extensions.Input
         }
 
         /// TODO again this might need to be more nuanced for things like continuous buttons.
-        /// <inheritdoc cref="IRLActionInputAdaptor.WriteToInputEventForAction"/>
-        public void WriteToInputEventForAction(InputEventPtr eventPtr, InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
+        /// <inheritdoc cref="IRLActionInputAdaptor.QueueInputEventForAction"/>
+        public void QueueInputEventForAction(InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
-            ((ButtonControl)control).WriteValueIntoEvent((float)val, eventPtr);
+            InputSystem.QueueDeltaStateEvent(control, (byte)val);
         }
 
         /// <inheritdoc cref="IRLActionInputAdaptor.WriteToHeuristic"/>>

com.unity.ml-agents.extensions/Runtime/Input/Adaptors/DoubleInputActionAdaptor.cs

 #if MLA_INPUT_SYSTEM && UNITY_2019_4_OR_NEWER
 using Unity.MLAgents.Actuators;
 using UnityEngine.InputSystem;
-using UnityEngine.InputSystem.Controls;
 using UnityEngine.InputSystem.LowLevel;
 
 namespace Unity.MLAgents.Extensions.Input
             return ActionSpec.MakeContinuous(1);
         }
 
-        /// <inheritdoc cref="IRLActionInputAdaptor.WriteToInputEventForAction"/>
-        public void WriteToInputEventForAction(InputEventPtr eventPtr, InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
+        /// <inheritdoc cref="IRLActionInputAdaptor.QueueInputEventForAction"/>
+        public void QueueInputEventForAction(InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
-            ((DoubleControl)control).WriteValueIntoEvent((double)val, eventPtr);
+            InputSystem.QueueDeltaStateEvent(control,(double)val);
         }
 
         /// <inheritdoc cref="IRLActionInputAdaptor.WriteToHeuristic"/>

com.unity.ml-agents.extensions/Runtime/Input/Adaptors/FloatInputActionAdaptor.cs

             return ActionSpec.MakeContinuous(1);
         }
 
-        /// <inheritdoc cref="IRLActionInputAdaptor.WriteToInputEventForAction"/>
-        public void WriteToInputEventForAction(InputEventPtr eventPtr, InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
+        /// <inheritdoc cref="IRLActionInputAdaptor.QueueInputEventForAction"/>
+        public void QueueInputEventForAction(InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
-            control.WriteValueIntoEvent(val, eventPtr);
+            InputSystem.QueueDeltaStateEvent(control, val);
         }
 
         /// <inheritdoc cref="IRLActionInputAdaptor.WriteToHeuristic"/>

com.unity.ml-agents.extensions/Runtime/Input/Adaptors/IntegerInputActionAdaptor.cs

             return ActionSpec.MakeDiscrete(2);
         }
 
-        /// <inheritdoc cref="IRLActionInputAdaptor.WriteToInputEventForAction"/>
-        public void WriteToInputEventForAction(InputEventPtr eventPtr, InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
+        /// <inheritdoc cref="IRLActionInputAdaptor.QueueInputEventForAction"/>
+        public void QueueInputEventForAction(InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
-            control.WriteValueIntoEvent(val, eventPtr);
+            InputSystem.QueueDeltaStateEvent(control, val);
         }
 
         /// <inheritdoc cref="IRLActionInputAdaptor.WriteToHeuristic"/>

com.unity.ml-agents.extensions/Runtime/Input/Adaptors/Vector2InputActionAdaptor.cs

 using Unity.MLAgents.Actuators;
 using UnityEngine;
 using UnityEngine.InputSystem;
-using UnityEngine.InputSystem.Controls;
 using UnityEngine.InputSystem.LowLevel;
 
 namespace Unity.MLAgents.Extensions.Input
             return ActionSpec.MakeContinuous(2);
         }
 
-        /// <inheritdoc cref="IRLActionInputAdaptor.WriteToInputEventForAction"/>
-        public void WriteToInputEventForAction(InputEventPtr eventPtr, InputAction action,
+        /// <inheritdoc cref="IRLActionInputAdaptor.QueueInputEventForAction"/>
+        public void QueueInputEventForAction(InputAction action,
             InputControl control,
             ActionSpec actionSpec,
             in ActionBuffers actionBuffers)
-            control.WriteValueIntoEvent(new Vector2(x, y), eventPtr);
+            InputSystem.QueueDeltaStateEvent(control, new Vector2(x, y));
         }
 
         /// <inheritdoc cref="IRLActionInputAdaptor.WriteToHeuristic"/>

com.unity.ml-agents.extensions/Runtime/Input/IRLActionInputAdaptor.cs

 using System;
 using Unity.MLAgents.Actuators;
 using UnityEngine.InputSystem;
-using UnityEngine.InputSystem.LowLevel;
 
 namespace Unity.MLAgents.Extensions.Input
 {
         /// <summary>
         /// Translates data from the <see cref="ActionBuffers"/> object to the <see cref="InputSystem"/>.
         /// </summary>
-        /// <param name="eventPtr">The Event pointer to write to.</param>
-        void WriteToInputEventForAction(InputEventPtr eventPtr, InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers);
+        void QueueInputEventForAction(InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers);
 
         /// <summary>
         /// Writes data from the <paramref name="action"/> to the <paramref name="actionBuffers"/>.

com.unity.ml-agents.extensions/Runtime/Input/InputActionActuator.cs

         readonly BehaviorParameters m_BehaviorParameters;
         readonly InputAction m_Action;
         readonly IRLActionInputAdaptor m_InputAdaptor;
-        InputActuatorEventContext m_InputActuatorEventContext;
         InputDevice m_Device;
         InputControl m_Control;
 
         ///     via the <see cref="IRLActionInputAdaptor"/>.</param>
         /// <param name="adaptor">The <see cref="IRLActionInputAdaptor"/> that will convert data between ML-Agents
         ///     and the <see cref="InputSystem"/>.</param>
-        /// <param name="inputActuatorEventContext">The object that will provide the event ptr to write to.</param>
-                                   IRLActionInputAdaptor adaptor,
-                                   InputActuatorEventContext inputActuatorEventContext)
+                                   IRLActionInputAdaptor adaptor)
-            m_InputActuatorEventContext = inputActuatorEventContext;
             ActionSpec = adaptor.GetActionSpecForInputAction(m_Action);
             m_Device = inputDevice;
             m_Control = m_Device?.GetChildControl(m_Action.name);
             Profiler.BeginSample("InputActionActuator.OnActionReceived");
             if (!m_BehaviorParameters.IsInHeuristicMode())
             {
-                using (m_InputActuatorEventContext.GetEventForFrame(out var eventPtr))
-                {
-                    m_InputAdaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, ActionSpec, actionBuffers);
-                }
-
+                m_InputAdaptor.QueueInputEventForAction(m_Action, m_Control, ActionSpec, actionBuffers);
             }
             Profiler.EndSample();
         }

com.unity.ml-agents.extensions/Runtime/Input/InputActuatorComponent.cs

 #if MLA_INPUT_SYSTEM && UNITY_2019_4_OR_NEWER
 using System;
 using System.Collections.Generic;
-using Unity.Collections;
 using Unity.MLAgents.Actuators;
 using Unity.MLAgents.Policies;
 using UnityEngine;
-using UnityEngine.InputSystem.LowLevel;
 using UnityEngine.InputSystem.Layouts;
 using UnityEngine.InputSystem.Utilities;
 #if UNITY_EDITOR
             get
             {
 #if UNITY_EDITOR
-                if (!EditorApplication.isPlaying && m_ActionSpec.NumContinuousActions == 0
-                    && m_ActionSpec.BranchSizes == null
-                    || m_ActionSpec.BranchSizes.Length == 0)
-                {
-                    FindNeededComponents();
-                    var actuators = CreateActuatorsFromMap(m_InputAsset.FindActionMap(m_PlayerInput.defaultActionMap),
-                        m_BehaviorParameters,
-                        null,
-                        InputActuatorEventContext.s_EditorContext);
-                    m_ActionSpec = CombineActuatorActionSpecs(actuators);
-
-                }
+                FindNeededComponents();
+                var actuators = CreateActuatorsFromMap(m_InputAsset.FindActionMap(m_PlayerInput.defaultActionMap), m_BehaviorParameters, null);
+                m_ActionSpec = CombineActuatorActionSpecs(actuators);
 #endif
                 return m_ActionSpec;
             }
             RegisterLayoutBuilder(inputActionMap, m_LayoutName);
             m_Device = InputSystem.AddDevice(m_LayoutName);
 
-            var context = new InputActuatorEventContext(inputActionMap.actions.Count, m_Device);
-            m_Actuators = CreateActuatorsFromMap(inputActionMap, m_BehaviorParameters, m_Device, context);
+            m_Actuators = CreateActuatorsFromMap(inputActionMap, m_BehaviorParameters, m_Device);
 
             UpdateDeviceBinding(m_BehaviorParameters.IsInHeuristicMode());
             inputActionMap.Enable();
 
         internal static IActuator[] CreateActuatorsFromMap(InputActionMap inputActionMap,
             BehaviorParameters behaviorParameters,
-            InputDevice inputDevice,
-            InputActuatorEventContext context)
+            InputDevice inputDevice)
         {
             var actuators = new IActuator[inputActionMap.actions.Count];
             for (var i = 0; i < inputActionMap.actions.Count; i++)
                 var adaptor = (IRLActionInputAdaptor)Activator.CreateInstance(controlTypeToAdaptorType[actionLayout.type]);
-                actuators[i] = new InputActionActuator(inputDevice, behaviorParameters, action, adaptor, context);
+                actuators[i] = new InputActionActuator(inputDevice, behaviorParameters, action, adaptor);
 
                 // Reasonably, the input system starts adding numbers after the first none numbered name
                 // is added.  So for device ID of 0, we use the empty string in the path.
                     action.processors,
                     mlAgentsControlSchemeName);
-                action.bindingMask = InputBinding.MaskByGroup(mlAgentsControlSchemeName);
             }
             return actuators;
         }
             m_PlayerInput = null;
             m_BehaviorParameters = null;
             m_Device = null;
-        }
-
-        int m_ActuatorsWrittenToEvent;
-        NativeArray<byte> m_InputBufferForFrame;
-        InputEventPtr m_InputEventPtrForFrame;
-        public InputEventPtr GetEventForFrame()
-        {
-#if UNITY_EDITOR
-            if (!EditorApplication.isPlaying)
-            {
-                return new InputEventPtr();
-            }
-#endif
-            if (m_ActuatorsWrittenToEvent % m_Actuators.Length == 0 || !m_InputEventPtrForFrame.valid)
-            {
-                m_ActuatorsWrittenToEvent = 0;
-                m_InputEventPtrForFrame = new InputEventPtr();
-                m_InputBufferForFrame = StateEvent.From(m_Device, out m_InputEventPtrForFrame);
-            }
-
-            return m_InputEventPtrForFrame;
-        }
-
-        public void EventProcessedInFrame()
-        {
-#if UNITY_EDITOR
-            if (!EditorApplication.isPlaying)
-            {
-                return;
-            }
-#endif
-            m_ActuatorsWrittenToEvent++;
-            if (m_ActuatorsWrittenToEvent == m_Actuators.Length && m_InputEventPtrForFrame.valid)
-            {
-                InputSystem.QueueEvent(m_InputEventPtrForFrame);
-                m_InputBufferForFrame.Dispose();
-            }
         }
     }
 }

com.unity.ml-agents.extensions/Runtime/Input/Unity.ML-Agents.Extensions.Input.asmdef

     "versionDefines": [
         {
             "name": "com.unity.inputsystem",
-            "expression": "1.1.0-preview.3",
+            "expression": "1.1.0-preview",
-}
+}

com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/ButtonInputActionAdaptorTests.cs

         public void TestQueueEvent()
         {
             var actionBuffers = new ActionBuffers(ActionSegment<float>.Empty, new ActionSegment<int>(new[] { 1 }));
-            var context = new InputActuatorEventContext(1, m_Device);
-            using (context.GetEventForFrame(out var eventPtr))
-            {
-                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
-            }
+            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
             InputSystem.Update();
             var val = m_Action.ReadValue<float>();
             Assert.IsTrue(Mathf.Approximately(1f, val));
         public void TestWriteToHeuristic()
         {
             var actionBuffers = new ActionBuffers(ActionSegment<float>.Empty, new ActionSegment<int>(new[] { 1 }));
-            var context = new InputActuatorEventContext(1, m_Device);
-            using (context.GetEventForFrame(out var eventPtr))
-            {
-                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
-            }
+            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
             InputSystem.Update();
             var buffer = new ActionBuffers(ActionSegment<float>.Empty, new ActionSegment<int>(new[] { 1 }));
             m_Adaptor.WriteToHeuristic(m_Action, buffer);

com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/DoubleInputActionAdaptorTests.cs

         public void TestQueueEvent()
         {
             var actionBuffers = new ActionBuffers(new ActionSegment<float>(new[] { 1f }), ActionSegment<int>.Empty);
-            var context = new InputActuatorEventContext(1, m_Device);
-            using (context.GetEventForFrame(out var eventPtr))
-            {
-                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
-            }
+            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
             InputSystem.Update();
             Assert.IsTrue(Mathf.Approximately(1f, (float)m_Action.ReadValue<double>()));
         }
         {
             var actionBuffers = new ActionBuffers(new ActionSegment<float>(new[] { 1f }), ActionSegment<int>.Empty);
-            var context = new InputActuatorEventContext(1, m_Device);
-            using (context.GetEventForFrame(out var eventPtr))
-            {
-                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
-            }
+            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
             InputSystem.Update();
             var buffer = new ActionBuffers(new ActionSegment<float>(new[] { 1f }), ActionSegment<int>.Empty);
             m_Adaptor.WriteToHeuristic(m_Action, buffer);

com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/FloatInputActionAdapatorTests.cs

         public void TestQueueEvent()
         {
             var actionBuffers = new ActionBuffers(new ActionSegment<float>(new[] { 1f }), ActionSegment<int>.Empty);
-            var context = new InputActuatorEventContext(1, m_Device);
-            using (context.GetEventForFrame(out var eventPtr))
-            {
-                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
-            }
+            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
             InputSystem.Update();
             var val = m_Action.ReadValue<float>();
             Assert.IsTrue(Mathf.Approximately(1f, val));
         public void TestWriteToHeuristic()
         {
             var actionBuffers = new ActionBuffers(new ActionSegment<float>(new[] { 1f }), ActionSegment<int>.Empty);
-            var context = new InputActuatorEventContext(1, m_Device);
-            using (context.GetEventForFrame(out var eventPtr))
-            {
-                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
-            }
+            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
             InputSystem.Update();
             var buffer = new ActionBuffers(new ActionSegment<float>(new[] { 1f }), ActionSegment<int>.Empty);
             m_Adaptor.WriteToHeuristic(m_Action, buffer);

com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/IntegerInputActionAdaptorTests.cs

         public void TestQueueEvent()
         {
             var actionBuffers = new ActionBuffers(ActionSegment<float>.Empty, new ActionSegment<int>(new[] { 1 }));
-            var context = new InputActuatorEventContext(1, m_Device);
-            using (context.GetEventForFrame(out var eventPtr))
-            {
-                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
-            }
+            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
             InputSystem.Update();
             var val = m_Action.ReadValue<int>();
             Assert.IsTrue(val == 1);
         public void TestWriteToHeuristic()
         {
             var actionBuffers = new ActionBuffers(ActionSegment<float>.Empty, new ActionSegment<int>(new[] { 1 }));
-            var context = new InputActuatorEventContext(1, m_Device);
-            using (context.GetEventForFrame(out var eventPtr))
-            {
-                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
-            }
+            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
             InputSystem.Update();
             var buffer = new ActionBuffers(ActionSegment<float>.Empty, new ActionSegment<int>(new int[1]));
             m_Adaptor.WriteToHeuristic(m_Action, buffer);

com.unity.ml-agents.extensions/Tests/Runtime/Input/Adaptors/Vector2InputActionAdaptorTests.cs

         public void TestQueueEvent()
         {
             var actionBuffers = new ActionBuffers(new ActionSegment<float>(new[] { 0f, 1f }), ActionSegment<int>.Empty);
-            var context = new InputActuatorEventContext(1, m_Device);
-            using (context.GetEventForFrame(out var eventPtr))
-            {
-                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
-            }
+            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
             InputSystem.Update();
             var val = m_Action.ReadValue<Vector2>();
             Assert.IsTrue(Mathf.Approximately(0f, val.x));
         public void TestWriteToHeuristic()
         {
             var actionBuffers = new ActionBuffers(new ActionSegment<float>(new[] { 0f, 1f }), ActionSegment<int>.Empty);
-            var context = new InputActuatorEventContext(1, m_Device);
-            using (context.GetEventForFrame(out var eventPtr))
-            {
-                m_Adaptor.WriteToInputEventForAction(eventPtr, m_Action, m_Control, new ActionSpec(), actionBuffers);
-            }
+            m_Adaptor.QueueInputEventForAction(m_Action, m_Control, new ActionSpec(), actionBuffers);
             InputSystem.Update();
             var buffer = new ActionBuffers(new ActionSegment<float>(new float[2]), ActionSegment<int>.Empty);
             m_Adaptor.WriteToHeuristic(m_Action, buffer);

com.unity.ml-agents.extensions/Tests/Runtime/Input/InputActionActuatorTests.cs

 using Unity.MLAgents.Policies;
 using UnityEngine;
 using UnityEngine.InputSystem;
-using UnityEngine.InputSystem.LowLevel;
-        public bool eventWritten;
+        public bool eventQueued;
         public bool writtenToHeuristic;
 
         public ActionSpec GetActionSpecForInputAction(InputAction action)
 
-        public void WriteToInputEventForAction(InputEventPtr eventPtr, InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
+        public void QueueInputEventForAction(InputAction action, InputControl control, ActionSpec actionSpec, in ActionBuffers actionBuffers)
-            eventWritten = true;
+            eventQueued = true;
         }
 
         public void WriteToHeuristic(InputAction action, in ActionBuffers actionBuffers)
 
         public void Reset()
         {
-            eventWritten = false;
+            eventQueued = false;
             writtenToHeuristic = false;
         }
     }
             m_BehaviorParameters = go.AddComponent<BehaviorParameters>();
             var action = new InputAction("action");
             m_Adaptor = new TestAdaptor();
-            m_Actuator = new InputActionActuator(null, m_BehaviorParameters, action, m_Adaptor, new InputActuatorEventContext(1, InputSystem.AddDevice<Gamepad>()));
+            m_Actuator = new InputActionActuator(null, m_BehaviorParameters, action, m_Adaptor);
         }
 
         [Test]
             m_Actuator.OnActionReceived(new ActionBuffers());
             m_Actuator.Heuristic(new ActionBuffers());
-            Assert.IsFalse(m_Adaptor.eventWritten);
+            Assert.IsFalse(m_Adaptor.eventQueued);
-            Assert.IsFalse(m_Adaptor.eventWritten);
+            Assert.IsFalse(m_Adaptor.eventQueued);
-            Assert.IsTrue(m_Adaptor.eventWritten);
+            Assert.IsTrue(m_Adaptor.eventQueued);
             m_Adaptor.Reset();
 
             Assert.AreEqual(m_Actuator.Name, "InputActionActuator-action");

com.unity.ml-agents.extensions/Tests/Runtime/Input/InputActuatorComponentTests.cs

 
             var device = InputSystem.AddDevice("TestLayout");
 
-            var actuators = InputActuatorComponent.CreateActuatorsFromMap(inputActionMap, m_BehaviorParameters, device, new InputActuatorEventContext());
+            var actuators = InputActuatorComponent.CreateActuatorsFromMap(inputActionMap, m_BehaviorParameters, device);
             Assert.IsTrue(actuators.Length == 2);
             Assert.IsTrue(actuators[0].ActionSpec.Equals(ActionSpec.MakeContinuous(2)));
             Assert.IsTrue(actuators[1].ActionSpec.NumDiscreteActions == 1);

com.unity.ml-agents.extensions/Tests/Runtime/Input/Unity.ML-Agents.Extensions.Input.Tests.Runtime.asmdef

     "versionDefines": [
         {
             "name": "com.unity.inputsystem",
-            "expression": "1.1.0-preview.3",
+            "expression": "1.1.0",
             "define": "MLA_INPUT_TESTS"
         }
     ],

com.unity.ml-agents.extensions/package.json

 {
   "name": "com.unity.ml-agents.extensions",
   "displayName": "ML Agents Extensions",
-  "version": "0.2.0-preview",
+  "version": "0.1.0-preview",
-    "com.unity.ml-agents": "1.8.1-preview"
+    "com.unity.ml-agents": "1.8.0-preview"
   }
 }

com.unity.ml-agents/.gitignore

 /Assets/Plugins*
 /Assets/Demonstrations*
 /csharp_timers.json
-/Samples/
-/Samples.meta
-*.api.meta
+*.api.meta

com.unity.ml-agents/CHANGELOG.md

 and this project adheres to
 [Semantic Versioning](http://semver.org/spec/v2.0.0.html).
 
-
 ## [Unreleased]
 ### Major Changes
 #### com.unity.ml-agents (C#)
 #### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
 #### ml-agents / ml-agents-envs / gym-unity (Python)
 - The `encoding_size` setting for RewardSignals has been deprecated. Please use `network_settings` instead. (#4982)
-- Sensor names are now passed through to `ObservationSpec.name`. (#5036)
+- The `cattrs` version dependency was updated to allow `>=1.1.0` on Python 3.8 or higher. (#4821)
-#### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
+#### com.unity.ml-agents (C#)
-- Made the error message when observations of different shapes are sent to the trainer clearer. (#5030)
-
-## [1.8.1-preview] - 2021-03-08
-### Minor Changes
-#### ml-agents / ml-agents-envs / gym-unity (Python)
-- The `cattrs` version dependency was updated to allow `>=1.1.0` on Python 3.8 or higher. (#4821)
-
-### Bug Fixes
-#### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
-- Fix an issue where queuing InputEvents overwrote data from previous events in the same frame. (#5034)
 
 ## [1.8.0-preview] - 2021-02-17
 ### Major Changes

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

 [unity ML-Agents Toolkit]: https://github.com/Unity-Technologies/ml-agents
 [unity inference engine]: https://docs.unity3d.com/Packages/com.unity.barracuda@latest/index.html
 [package manager documentation]: https://docs.unity3d.com/Manual/upm-ui-install.html
-[installation instructions]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Installation.md
+[installation instructions]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/Installation.md
-[ML-Agents GitHub repo]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/com.unity.ml-agents.extensions
+[ML-Agents GitHub repo]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/com.unity.ml-agents.extensions

com.unity.ml-agents/Runtime/MultiAgentGroupIdCounter.cs.meta

 fileFormatVersion: 2
-guid: 3744ac27d956e43e1a39c7ba2550ab82
+guid: 5661ffdb6c7704e84bc785572dcd5bd1
 MonoImporter:
   externalObjects: {}
   serializedVersion: 2

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

     /// <summary>
     /// A basic class implementation of MultiAgentGroup.
     /// </summary>
-    internal class SimpleMultiAgentGroup : IMultiAgentGroup, IDisposable
+    public class SimpleMultiAgentGroup : IMultiAgentGroup, IDisposable
     {
         readonly int m_Id = MultiAgentGroupIdCounter.GetGroupId();
         HashSet<Agent> m_Agents = new HashSet<Agent>();

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

  * API. For more information on each of these entities, in addition to how to
  * set-up a learning environment and train the behavior of characters in a
  * Unity scene, please browse our documentation pages on GitHub:
- * https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/docs/
+ * https://github.com/Unity-Technologies/ml-agents/tree/release_13_docs/docs/
  */
 
 namespace Unity.MLAgents
     /// fall back to inference or heuristic decisions. (You can also set agents to always use
     /// inference or heuristics.)
     /// </remarks>
-    [HelpURL("https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/" +
+    [HelpURL("https://github.com/Unity-Technologies/ml-agents/tree/release_13_docs/" +
         "docs/Learning-Environment-Design.md")]
     public class Academy : IDisposable
     {
         /// Unity package version of com.unity.ml-agents.
         /// This must match the version string in package.json and is checked in a unit test.
         /// </summary>
-        internal const string k_PackageVersion = "1.8.1-preview";
+        internal const string k_PackageVersion = "1.8.0-preview";
 
         const int k_EditorTrainingPort = 5004;
 

com.unity.ml-agents/Runtime/Actuators/IActionReceiver.cs

         ///
         /// See [Agents - Actions] for more information on masking actions.
         ///
-        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Learning-Environment-Design-Agents.md#actions
+        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/Learning-Environment-Design-Agents.md#actions
         /// </remarks>
         /// <seealso cref="IActionReceiver.OnActionReceived"/>
         void WriteDiscreteActionMask(IDiscreteActionMask actionMask);

com.unity.ml-agents/Runtime/Actuators/IDiscreteActionMask.cs

         ///
         /// See [Agents - Actions] for more information on masking actions.
         ///
-        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Learning-Environment-Design-Agents.md#actions
+        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/Learning-Environment-Design-Agents.md#actions
         /// </remarks>
         /// <param name="branch">The branch for which the actions will be masked.</param>
         /// <param name="actionIndices">The indices of the masked actions.</param>

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

     /// [OnDisable()]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.OnDisable.html]
     /// [OnBeforeSerialize()]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.OnBeforeSerialize.html
     /// [OnAfterSerialize()]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.OnAfterSerialize.html
-    /// [Agents]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Learning-Environment-Design-Agents.md
-    /// [Reinforcement Learning in Unity]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Learning-Environment-Design.md
+    /// [Agents]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/Learning-Environment-Design-Agents.md
+    /// [Reinforcement Learning in Unity]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/Learning-Environment-Design.md
-    /// [Unity ML-Agents Toolkit manual]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Readme.md
+    /// [Unity ML-Agents Toolkit manual]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/Readme.md
-    [HelpURL("https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/" +
+    [HelpURL("https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/" +
         "docs/Learning-Environment-Design-Agents.md")]
     [Serializable]
     [RequireComponent(typeof(BehaviorParameters))]
         /// for information about mixing reward signals from curiosity and Generative Adversarial
         /// Imitation Learning (GAIL) with rewards supplied through this method.
         ///
-        /// [Agents - Rewards]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Learning-Environment-Design-Agents.md#rewards
-        /// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/ML-Agents-Overview.md#a-quick-note-on-reward-signals
+        /// [Agents - Rewards]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/Learning-Environment-Design-Agents.md#rewards
+        /// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/ML-Agents-Overview.md#a-quick-note-on-reward-signals
         /// </remarks>
         /// <param name="reward">The new value of the reward.</param>
         public void SetReward(float reward)
         /// for information about mixing reward signals from curiosity and Generative Adversarial
         /// Imitation Learning (GAIL) with rewards supplied through this method.
         ///
-        /// [Agents - Rewards]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Learning-Environment-Design-Agents.md#rewards
-        /// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/ML-Agents-Overview.md#a-quick-note-on-reward-signals
+        /// [Agents - Rewards]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/Learning-Environment-Design-Agents.md#rewards
+        /// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/ML-Agents-Overview.md#a-quick-note-on-reward-signals
         ///</remarks>
         /// <param name="increment">Incremental reward value.</param>
         public void AddReward(float increment)
         /// implementing a simple heuristic function can aid in debugging agent actions and interactions
         /// with its environment.
         ///
-        /// [Demonstration Recorder]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Learning-Environment-Design-Agents.md#recording-demonstrations
-        /// [Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Learning-Environment-Design-Agents.md#actions
+        /// [Demonstration Recorder]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/Learning-Environment-Design-Agents.md#recording-demonstrations
+        /// [Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/Learning-Environment-Design-Agents.md#actions
         /// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
         /// </remarks>
         /// <example>
         /// For more information about observations, see [Observations and Sensors].
         ///
         /// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
-        /// [Observations and Sensors]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Learning-Environment-Design-Agents.md#observations-and-sensors
+        /// [Observations and Sensors]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/Learning-Environment-Design-Agents.md#observations-and-sensors
         /// </remarks>
         public virtual void CollectObservations(VectorSensor sensor)
         {
         ///
         /// See [Agents - Actions] for more information on masking actions.
         ///
-        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Learning-Environment-Design-Agents.md#actions
+        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/Learning-Environment-Design-Agents.md#actions
         /// </remarks>
         /// <seealso cref="IActionReceiver.OnActionReceived"/>
         public virtual void WriteDiscreteActionMask(IDiscreteActionMask actionMask)
         ///
         /// For more information about implementing agent actions see [Agents - Actions].
         ///
-        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Learning-Environment-Design-Agents.md#actions
+        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/Learning-Environment-Design-Agents.md#actions
         /// </remarks>
         /// <param name="actions">
         /// Struct containing the buffers of actions to be executed at this step.

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

             if (dimensionPropertySensor != null)
             {
                 var dimensionProperties = dimensionPropertySensor.GetDimensionProperties();
+                int[] intDimensionProperties = new int[dimensionProperties.Length];
                 for (int i = 0; i < dimensionProperties.Length; i++)
                 {
                     observationProto.DimensionProperties.Add((int)dimensionProperties[i]);
                 }
             }
             observationProto.Shape.AddRange(shape);
-            var sensorName = sensor.GetName();
-            if (!string.IsNullOrEmpty(sensorName))
-            {
-                observationProto.Name = sensorName;
-            }
 
             // Add the observation type, if any, to the observationProto
             var typeSensor = sensor as ITypedSensor;

com.unity.ml-agents/Runtime/Demonstrations/DemonstrationRecorder.cs

     /// See [Imitation Learning - Recording Demonstrations] for more information.
     ///
     /// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
-    /// [Imitation Learning - Recording Demonstrations]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs//Learning-Environment-Design-Agents.md#recording-demonstrations
+    /// [Imitation Learning - Recording Demonstrations]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs//Learning-Environment-Design-Agents.md#recording-demonstrations
     /// </remarks>
     [RequireComponent(typeof(Agent))]
     [AddComponentMenu("ML Agents/Demonstration Recorder", (int)MenuGroup.Default)]

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

         ///
         /// See [Agents - Actions] for more information on masking actions.
         ///
-        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Learning-Environment-Design-Agents.md#actions
+        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/Learning-Environment-Design-Agents.md#actions
         /// </remarks>
         /// <param name="branch">The branch for which the actions will be masked.</param>
         /// <param name="actionIndices">The indices of the masked actions.</param>

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

       byte[] descriptorData = global::System.Convert.FromBase64String(
           string.Concat(
             "CjRtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL29ic2VydmF0",
-            "aW9uLnByb3RvEhRjb21tdW5pY2F0b3Jfb2JqZWN0cyKPAwoQT2JzZXJ2YXRp",
+            "aW9uLnByb3RvEhRjb21tdW5pY2F0b3Jfb2JqZWN0cyKBAwoQT2JzZXJ2YXRp",
             "b25Qcm90bxINCgVzaGFwZRgBIAMoBRJEChBjb21wcmVzc2lvbl90eXBlGAIg",
             "ASgOMiouY29tbXVuaWNhdG9yX29iamVjdHMuQ29tcHJlc3Npb25UeXBlUHJv",
             "dG8SGQoPY29tcHJlc3NlZF9kYXRhGAMgASgMSAASRgoKZmxvYXRfZGF0YRgE",
             "b25fdHlwZRgHIAEoDjIqLmNvbW11bmljYXRvcl9vYmplY3RzLk9ic2VydmF0",
-            "aW9uVHlwZVByb3RvEgwKBG5hbWUYCCABKAkaGQoJRmxvYXREYXRhEgwKBGRh",
-            "dGEYASADKAJCEgoQb2JzZXJ2YXRpb25fZGF0YSopChRDb21wcmVzc2lvblR5",
-            "cGVQcm90bxIICgROT05FEAASBwoDUE5HEAEqRgoUT2JzZXJ2YXRpb25UeXBl",
-            "UHJvdG8SCwoHREVGQVVMVBAAEggKBEdPQUwQARIKCgZSRVdBUkQQAhILCgdN",
-            "RVNTQUdFEANCJaoCIlVuaXR5Lk1MQWdlbnRzLkNvbW11bmljYXRvck9iamVj",
-            "dHNiBnByb3RvMw=="));
+            "aW9uVHlwZVByb3RvGhkKCUZsb2F0RGF0YRIMCgRkYXRhGAEgAygCQhIKEG9i",
+            "c2VydmF0aW9uX2RhdGEqKQoUQ29tcHJlc3Npb25UeXBlUHJvdG8SCAoETk9O",
+            "RRAAEgcKA1BORxABKkYKFE9ic2VydmF0aW9uVHlwZVByb3RvEgsKB0RFRkFV",
+            "TFQQABIICgRHT0FMEAESCgoGUkVXQVJEEAISCwoHTUVTU0FHRRADQiWqAiJV",
+            "bml0eS5NTEFnZW50cy5Db21tdW5pY2F0b3JPYmplY3RzYgZwcm90bzM="));
-            new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.ObservationProto), global::Unity.MLAgents.CommunicatorObjects.ObservationProto.Parser, new[]{ "Shape", "CompressionType", "CompressedData", "FloatData", "CompressedChannelMapping", "DimensionProperties", "ObservationType", "Name" }, 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(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
       compressedChannelMapping_ = other.compressedChannelMapping_.Clone();
       dimensionProperties_ = other.dimensionProperties_.Clone();
       observationType_ = other.observationType_;
-      name_ = other.name_;
       switch (other.ObservationDataCase) {
         case ObservationDataOneofCase.CompressedData:
           CompressedData = other.CompressedData;
       }
     }
 
-    /// <summary>Field number for the "name" field.</summary>
-    public const int NameFieldNumber = 8;
-    private string name_ = "";
-    /// <summary>
-    /// Optional name of the observation.
-    /// This will be set to the ISensor name when writing,
-    /// and read into the ObservationSpec in the low-level API
-    /// </summary>
-    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
-    public string Name {
-      get { return name_; }
-      set {
-        name_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
-      }
-    }
-
     private object observationData_;
     /// <summary>Enum of possible cases for the "observation_data" oneof.</summary>
     public enum ObservationDataOneofCase {
       if(!compressedChannelMapping_.Equals(other.compressedChannelMapping_)) return false;
       if(!dimensionProperties_.Equals(other.dimensionProperties_)) return false;
       if (ObservationType != other.ObservationType) return false;
-      if (Name != other.Name) return false;
       if (ObservationDataCase != other.ObservationDataCase) return false;
       return Equals(_unknownFields, other._unknownFields);
     }
       hash ^= compressedChannelMapping_.GetHashCode();
       hash ^= dimensionProperties_.GetHashCode();
       if (ObservationType != 0) hash ^= ObservationType.GetHashCode();
-      if (Name.Length != 0) hash ^= Name.GetHashCode();
       hash ^= (int) observationDataCase_;
       if (_unknownFields != null) {
         hash ^= _unknownFields.GetHashCode();
         output.WriteRawTag(56);
         output.WriteEnum((int) ObservationType);
       }
-      if (Name.Length != 0) {
-        output.WriteRawTag(66);
-        output.WriteString(Name);
-      }
       if (_unknownFields != null) {
         _unknownFields.WriteTo(output);
       }
       if (ObservationType != 0) {
         size += 1 + pb::CodedOutputStream.ComputeEnumSize((int) ObservationType);
       }
-      if (Name.Length != 0) {
-        size += 1 + pb::CodedOutputStream.ComputeStringSize(Name);
-      }
       if (_unknownFields != null) {
         size += _unknownFields.CalculateSize();
       }
       dimensionProperties_.Add(other.dimensionProperties_);
       if (other.ObservationType != 0) {
         ObservationType = other.ObservationType;
-      }
-      if (other.Name.Length != 0) {
-        Name = other.Name;
       }
       switch (other.ObservationDataCase) {
         case ObservationDataOneofCase.CompressedData:
           }
           case 56: {
             observationType_ = (global::Unity.MLAgents.CommunicatorObjects.ObservationTypeProto) input.ReadEnum();
-            break;
-          }
-          case 66: {
-            Name = input.ReadString();
             break;
           }
         }

com.unity.ml-agents/Runtime/Policies/BehaviorParameters.cs

         [HideInInspector, SerializeField]
         BrainParameters m_BrainParameters = new BrainParameters();
 
-        /// <summary>
-        /// Delegate for receiving events about Policy Updates.
-        /// </summary>
-        /// <param name="isInHeuristicMode">Whether or not the current policy is running in heuristic mode.</param>
-        /// <summary>
-        /// Event that fires when an Agent's policy is updated.
-        /// </summary>
         internal event PolicyUpdated OnPolicyUpdated;
 
         /// <summary>

com.unity.ml-agents/package.json

 {
   "name": "com.unity.ml-agents",
   "displayName": "ML Agents",
-  "version": "1.8.1-preview",
+  "version": "1.8.0-preview",
   "unity": "2018.4",
   "description": "Use state-of-the-art machine learning to create intelligent character behaviors in any Unity environment (games, robotics, film, etc.).",
   "dependencies": {

config/imitation/CrawlerStatic.yaml

 behaviors:
-  Crawler:
+  CrawlerStatic:
     trainer_type: ppo
     hyperparameters:
       batch_size: 2024
         learning_rate: 0.0003
         use_actions: false
         use_vail: false
-        demo_path: Project/Assets/ML-Agents/Examples/Crawler/Demos/ExpertCrawler.demo
+        demo_path: Project/Assets/ML-Agents/Examples/Crawler/Demos/ExpertCrawlerSta.demo
     keep_checkpoints: 5
     max_steps: 10000000
     time_horizon: 1000
-      demo_path: Project/Assets/ML-Agents/Examples/Crawler/Demos/ExpertCrawler.demo
+      demo_path: Project/Assets/ML-Agents/Examples/Crawler/Demos/ExpertCrawlerSta.demo
       steps: 50000
       strength: 0.5
       samples_per_update: 0

config/ppo/FoodCollector.yaml

       learning_rate_schedule: linear
     network_settings:
       normalize: false
-      hidden_units: 256
-      num_layers: 1
+      hidden_units: 128
+      num_layers: 2
       vis_encode_type: simple
     reward_signals:
       extrinsic:

config/ppo/CrawlerDynamic.yaml

 behaviors:
-  Crawler:
+  CrawlerDynamic:
     trainer_type: ppo
     hyperparameters:
       batch_size: 2048

config/ppo/WalkerDynamic.yaml

 behaviors:
-  Walker:
+  WalkerDynamic:
     trainer_type: ppo
     hyperparameters:
       batch_size: 2048

config/ppo/WormDynamic.yaml

 behaviors:
-  Worm:
+  WormDynamic:
     trainer_type: ppo
     hyperparameters:
       batch_size: 2024

config/sac/FoodCollector.yaml

       learning_rate: 0.0003
       learning_rate_schedule: constant
       batch_size: 256
-      buffer_size: 2048
+      buffer_size: 500000
       buffer_init_steps: 0
       tau: 0.005
       steps_per_update: 10.0
     network_settings:
       normalize: false
-      hidden_units: 256
-      num_layers: 1
+      hidden_units: 128
+      num_layers: 2
       vis_encode_type: simple
     reward_signals:
       extrinsic:
     max_steps: 2000000
     time_horizon: 64
-    summary_freq: 60000
-    threaded: true
+    summary_freq: 10000
+    threaded: true

config/sac/CrawlerDynamic.yaml

 behaviors:
-  Crawler:
+  CrawlerDynamic:
     trainer_type: sac
     hyperparameters:
       learning_rate: 0.0003

config/sac/WalkerDynamic.yaml

 behaviors:
-  Walker:
+  WalkerDynamic:
     trainer_type: sac
     hyperparameters:
       learning_rate: 0.0003

config/sac/WormDynamic.yaml

 behaviors:
-  Worm:
+  WormDynamic:
     trainer_type: sac
     hyperparameters:
       learning_rate: 0.0003

docs/Installation-Anaconda-Windows.md

 the ml-agents Conda environment by typing `activate ml-agents`)_:
 
 ```sh
-git clone --branch release_14 https://github.com/Unity-Technologies/ml-agents.git
+git clone --branch release_13 https://github.com/Unity-Technologies/ml-agents.git
-The `--branch release_14` option will switch to the tag of the latest stable
+The `--branch release_13` option will switch to the tag of the latest stable
 release. Omitting that will get the `main` branch which is potentially
 unstable.
 
 connected to the Internet and then type in the Anaconda Prompt:
 
 ```console
-python -m pip install mlagents==0.24.1
+pip install mlagents
 ```
 
 This will complete the installation of all the required Python packages to run
 this, you can try:
 
 ```console
-python -m pip install mlagents==0.24.1 --no-cache-dir
+pip install mlagents --no-cache-dir
 ```
 
 This `--no-cache-dir` tells the pip to disable the cache.

docs/Installation.md

 of our tutorials / guides assume you have access to our example environments).
 
 ```sh
-git clone --branch release_14 https://github.com/Unity-Technologies/ml-agents.git
+git clone --branch release_13 https://github.com/Unity-Technologies/ml-agents.git
-The `--branch release_14` option will switch to the tag of the latest stable
-release. Omitting that will get the `main` branch which is potentially unstable.
+The `--branch release_13` option will switch to the tag of the latest stable
+release. Omitting that will get the `main` branch which is potentially
+unstable.
-back, make sure to clone the `main` branch (by omitting `--branch release_14`
+back, make sure to clone the `main` branch (by omitting `--branch release_13`
 from the command above). See our
 [Contributions Guidelines](../com.unity.ml-agents/CONTRIBUTING.md) for more
 information on contributing to the ML-Agents Toolkit.
 run from the command line:
 
 ```sh
-python -m pip install mlagents==0.24.1
+pip3 install mlagents
 ```
 
 Note that this will install `mlagents` from PyPi, _not_ from the cloned

docs/Learning-Environment-Design-Agents.md

 training process learns to control the speed of the Agent through this
 parameter.
 
-The [3DBall example](Learning-Environment-Examples.md#3dball-3d-balance-ball) uses
+The [Reacher example](Learning-Environment-Examples.md#reacher) uses
-![3DBall](images/balance.png)
+![reacher](images/reacher.png)
-These control values are applied as rotation to the cube:
+These control values are applied as torques to the bodies making up the arm:
-        var actionZ = 2f * Mathf.Clamp(actionBuffers.ContinuousActions[0], -1f, 1f);
-        var actionX = 2f * Mathf.Clamp(actionBuffers.ContinuousActions[1], -1f, 1f);
+        var torqueX = Mathf.Clamp(actionBuffers.ContinuousActions[0], -1f, 1f) * 150f;
+        var torqueZ = Mathf.Clamp(actionBuffers.ContinuousActions[1], -1f, 1f) * 150f;
+        m_RbA.AddTorque(new Vector3(torqueX, 0f, torqueZ));
-        gameObject.transform.Rotate(new Vector3(0, 0, 1), actionZ);
-        gameObject.transform.Rotate(new Vector3(1, 0, 0), actionX);
+        torqueX = Mathf.Clamp(actionBuffers.ContinuousActions[2], -1f, 1f) * 150f;
+        torqueZ = Mathf.Clamp(actionBuffers.ContinuousActions[3], -1f, 1f) * 150f;
+        m_RbB.AddTorque(new Vector3(torqueX, 0f, torqueZ));
     }
 ```
 

docs/Learning-Environment-Examples.md

   number of goals.
 - Benchmark Mean Reward: 0.8
 
+## Tennis
+
+![Tennis](images/tennis.png)
+
+- Set-up: Two-player game where agents control rackets to hit a ball over the
+  net.
+- Goal: The agents must hit the ball so that the opponent cannot hit a valid
+  return.
+- Agents: The environment contains two agent with same Behavior Parameters.
+  After training you can set the `Behavior Type` to `Heuristic Only` on one of
+  the Agent's Behavior Parameters to play against your trained model.
+- Agent Reward Function (independent):
+  - +1.0 To the agent that wins the point. An agent wins a point by preventing
+    the opponent from hitting a valid return.
+  - -1.0 To the agent who loses the point.
+- Behavior Parameters:
+  - Vector Observation space: 9 variables corresponding to position, velocity
+    and orientation of ball and racket.
+  - Actions: 3 continuous actions, corresponding to movement
+    toward net or away from net, jumping and rotation.
+  - Visual Observations: None
+- Float Properties: Three
+  - gravity: Magnitude of gravity
+    - Default: 9.81
+    - Recommended Minimum: 6
+    - Recommended Maximum: 20
+  - scale: Specifies the scale of the ball in the 3 dimensions (equal across the
+    three dimensions)
+    - Default: .5
+    - Recommended Minimum: 0.2
+    - Recommended Maximum: 5
+
 ## Push Block
 
 ![Push](images/push.png)
     block).
   - Actions: 1 discrete action branch with 7 actions, corresponding to turn clockwise
     and counterclockwise, move along four different face directions, or do nothing.
+  - Visual Observations (Optional): One first-person camera. Use
+    `VisualPushBlock` scene. **The visual observation version of this
+    environment does not train with the provided default training parameters.**
 - Float Properties: Four
   - block_scale: Scale of the block along the x and z dimensions
     - Default: 2
 - Float Properties: Four
 - Benchmark Mean Reward (Big & Small Wall): 0.8
 
+## Reacher
+
+![Reacher](images/reacher.png)
+
+- Set-up: Double-jointed arm which can move to target locations.
+- Goal: The agents must move its hand to the goal location, and keep it there.
+- Agents: The environment contains 10 agent with same Behavior Parameters.
+- Agent Reward Function (independent):
+  - +0.1 Each step agent's hand is in goal location.
+- Behavior Parameters:
+  - Vector Observation space: 26 variables corresponding to position, rotation,
+    velocity, and angular velocities of the two arm rigid bodies.
+  - Actions: 4 continuous actions, corresponding to torque
+    applicable to two joints.
+  - Visual Observations: None.
+- Float Properties: Five
+  - goal_size: radius of the goal zone
+    - Default: 5
+    - Recommended Minimum: 1
+    - Recommended Maximum: 10
+  - goal_speed: speed of the goal zone around the arm (in radians)
+    - Default: 1
+    - Recommended Minimum: 0.2
+    - Recommended Maximum: 4
+  - gravity
+    - Default: 9.81
+    - Recommended Minimum: 4
+    - Recommended Maximum: 20
+  - deviation: Magnitude of sinusoidal (cosine) deviation of the goal along the
+    vertical dimension
+    - Default: 0
+    - Recommended Minimum: 0
+    - Recommended Maximum: 5
+  - deviation_freq: Frequency of the cosine deviation of the goal along the
+    vertical dimension
+    - Default: 0
+    - Recommended Minimum: 0
+    - Recommended Maximum: 3
+- Benchmark Mean Reward: 30
+
 ## Crawler
 
 ![Crawler](images/crawler.png)
+  - `CrawlerDynamicTarget`- Goal direction is randomized.
+  - `CrawlerDynamicVariableSpeed`- Goal direction and walking speed are randomized.
+  - `CrawlerStaticTarget` - Goal direction is always forward.
+  - `CrawlerStaticVariableSpeed`- Goal direction is always forward. Walking speed is randomized
 - Agents: The environment contains 10 agents with same Behavior Parameters.
 - Agent Reward Function (independent):
   The reward function is now geometric meaning the reward each step is a product
     rotations for joints.
   - Visual Observations: None
 - Float Properties: None
-- Benchmark Mean Reward: 3000
+- Benchmark Mean Reward for `CrawlerDynamicTarget`: 2000
+- Benchmark Mean Reward for `CrawlerDynamicVariableSpeed`: 3000
+- Benchmark Mean Reward for `CrawlerStaticTarget`: 4000
+- Benchmark Mean Reward for `CrawlerStaticVariableSpeed`: 4000
 
 ## Worm
 
 - Goal: The agents must move its body toward the goal direction.
+  - `WormStaticTarget` - Goal direction is always forward.
+  - `WormDynamicTarget`- Goal direction is randomized.
 - Agents: The environment contains 10 agents with same Behavior Parameters.
 - Agent Reward Function (independent):
   The reward function is now geometric meaning the reward each step is a product
     rotations for joints.
   - Visual Observations: None
 - Float Properties: None
-- Benchmark Mean Reward: 800
+- Benchmark Mean Reward for `WormStaticTarget`: 1200
+- Benchmark Mean Reward for `WormDynamicTarget`: 800
 
 ## Food Collector
 
   - -1 for interaction with red spheres
 - Behavior Parameters:
   - Vector Observation space: 53 corresponding to velocity of agent (2), whether
-    agent is frozen and/or shot its laser (2), plus grid based perception of
-    objects around agent's forward direction (40 by 40 with 6 different categories).
+    agent is frozen and/or shot its laser (2), plus ray-based perception of
+    objects around agent's forward direction (49; 7 raycast angles with 7
+    measurements for each).
   - Actions:
     - 3 continuous actions correspond to Forward Motion, Side Motion and Rotation
     - 1 discrete acion branch for Laser with 2 possible actions corresponding to
     objects, goals, and walls.
   - Actions: 1 discrete action Branch, with 4 actions corresponding to agent
     rotation and forward/backward movement.
+  - Visual Observations (Optional): First-person view for the agent. Use
+    `VisualHallway` scene. **The visual observation version of this environment
+    does not train with the provided default training parameters.**
+## Bouncer
+
+![Bouncer](images/bouncer.png)
+
+- Set-up: Environment where the agent needs on-demand decision making. The agent
+  must decide how perform its next bounce only when it touches the ground.
+- Goal: Catch the floating green cube. Only has a limited number of jumps.
+- Agents: The environment contains one agent.
+- Agent Reward Function (independent):
+  - +1 For catching the green cube.
+  - -1 For bouncing out of bounds.
+  - -0.05 Times the action squared. Energy expenditure penalty.
+- Behavior Parameters:
+  - Vector Observation space: 6 corresponding to local position of agent and
+    green cube.
+  - Actions: 3 continuous actions corresponding to agent force applied for
+    the jump.
+  - Visual Observations: None
+- Float Properties: Two
+  - target_scale: The scale of the green cube in the 3 dimensions
+    - Default: 150
+    - Recommended Minimum: 50
+    - Recommended Maximum: 250
+- Benchmark Mean Reward: 10
+
 ## Soccer Twos
 
 ![SoccerTwos](images/soccer.png)
   correspond to articulation of the following body-parts: hips, chest, spine,
   head, thighs, shins, feet, arms, forearms and hands.
 - Goal: The agents must move its body toward the goal direction without falling.
+  - `WalkerDynamic`- Goal direction is randomized.
+  - `WalkerDynamicVariableSpeed`- Goal direction and walking speed are randomized.
+  - `WalkerStatic` - Goal direction is always forward.
+  - `WalkerStaticVariableSpeed` - Goal direction is always forward. Walking
+     speed is randomized
 - Agents: The environment contains 10 independent agents with same Behavior
   Parameters.
 - Agent Reward Function (independent):
     - Default: 8
     - Recommended Minimum: 3
     - Recommended Maximum: 20
-- Benchmark Mean Reward : 2500
+- Benchmark Mean Reward for `WalkerDynamic`: 2500
+- Benchmark Mean Reward for `WalkerDynamicVariableSpeed`: 2500
+- Benchmark Mean Reward for `WalkerStatic`: 3500
+- Benchmark Mean Reward for `WalkerStaticVariableSpeed`: 3500
+
 
 
 ## Pyramids
     state.
   - Actions: 1 discrete action branch, with 4 actions corresponding to agent rotation and
     forward/backward movement.
+  - Visual Observations (Optional): First-person camera per-agent. Us
+    `VisualPyramids` scene. **The visual observation version of this environment
+    does not train with the provided default training parameters.**
 - Float Properties: None
 - Benchmark Mean Reward: 1.75
 

docs/Training-Configuration-File.md

 | `network_settings -> hidden_units`           | (default = `128`) Number of units in the hidden layers of the neural network. Correspond to how many units are in each fully connected layer of the neural network. For simple problems where the correct action is a straightforward combination of the observation inputs, this should be small. For problems where the action is a very complex interaction between the observation variables, this should be larger. <br><br> Typical range: `32` - `512`                                                                                                                                                                                                                                                                                    |
 | `network_settings -> num_layers`             | (default = `2`) The number of hidden layers in the neural network. Corresponds to how many hidden layers are present after the observation input, or after the CNN encoding of the visual observation. For simple problems, fewer layers are likely to train faster and more efficiently. More layers may be necessary for more complex control problems. <br><br> Typical range: `1` - `3`                                                                                                                                                                                                                                                                                                                                                    |
 | `network_settings -> normalize`              | (default = `false`) Whether normalization is applied to the vector observation inputs. This normalization is based on the running average and variance of the vector observation. Normalization can be helpful in cases with complex continuous control problems, but may be harmful with simpler discrete control problems.                                                                                                                                                                                                                                                                                                                                                                                                                       |
-| `network_settings -> vis_encode_type`       | (default = `simple`) Encoder type for encoding visual observations. <br><br> `simple` (default) uses a simple encoder which consists of two convolutional layers, `nature_cnn` uses the CNN implementation proposed by [Mnih et al.](https://www.nature.com/articles/nature14236), consisting of three convolutional layers, and `resnet` uses the [IMPALA Resnet](https://arxiv.org/abs/1802.01561) consisting of three stacked layers, each with two residual blocks, making a much larger network than the other two. `match3` is a smaller CNN ([Gudmundsoon et al.](https://www.researchgate.net/publication/328307928_Human-Like_Playtesting_with_Deep_Learning)) that is optimized for board games, and can be used down to visual observation sizes of 5x5.                                                                                                                                                                                              |
+| `network_settings -> vis_encoder_type`       | (default = `simple`) Encoder type for encoding visual observations. <br><br> `simple` (default) uses a simple encoder which consists of two convolutional layers, `nature_cnn` uses the CNN implementation proposed by [Mnih et al.](https://www.nature.com/articles/nature14236), consisting of three convolutional layers, and `resnet` uses the [IMPALA Resnet](https://arxiv.org/abs/1802.01561) consisting of three stacked layers, each with two residual blocks, making a much larger network than the other two. `match3` is a smaller CNN ([Gudmundsoon et al.](https://www.researchgate.net/publication/328307928_Human-Like_Playtesting_with_Deep_Learning)) that is optimized for board games, and can be used down to visual observation sizes of 5x5.                                                                                                                                                                                              |
 
 
 ## Trainer-specific Configurations

docs/Training-ML-Agents.md

 
     # Configuration of the neural network (common to PPO/SAC)
     network_settings:
-      vis_encode_type: simple
+      vis_encoder_type: simple
       normalize: false
       hidden_units: 128
       num_layers: 2

docs/Training-on-Amazon-Web-Service.md

 2. Clone the ML-Agents repo and install the required Python packages
 
    ```sh
-   git clone --branch release_14 https://github.com/Unity-Technologies/ml-agents.git
+   git clone --branch release_13 https://github.com/Unity-Technologies/ml-agents.git
    cd ml-agents/ml-agents/
    pip3 install -e .
    ```

docs/Training-on-Microsoft-Azure.md

    instance, and set it as the working directory.
 2. Install the required packages:
    Torch: `pip3 install torch==1.7.0 -f https://download.pytorch.org/whl/torch_stable.html` and
-   MLAgents: `python -m pip install mlagents==0.24.1`
+   MLAgents: `pip3 install mlagents`
 
 ## Testing
 

docs/Unity-Inference-Engine.md

 loading expects certain conventions for constants and tensor names. While it is
 possible to construct a model that follows these conventions, we don't provide
 any additional help for this. More details can be found in
-[TensorNames.cs](https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/com.unity.ml-agents/Runtime/Inference/TensorNames.cs)
+[TensorNames.cs](https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/com.unity.ml-agents/Runtime/Inference/TensorNames.cs)
-[BarracudaModelParamLoader.cs](https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/com.unity.ml-agents/Runtime/Inference/BarracudaModelParamLoader.cs).
+[BarracudaModelParamLoader.cs](https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/com.unity.ml-agents/Runtime/Inference/BarracudaModelParamLoader.cs).
 
 If you wish to run inference on an externally trained model, you should use
 Barracuda directly, instead of trying to run it through ML-Agents.

ml-agents-envs/README.md

 Install the `mlagents_envs` package with:
 
 ```sh
-python -m pip install mlagents_envs==0.24.1
+pip3 install mlagents_envs
 ```
 
 ## Usage & More Information

ml-agents-envs/mlagents_envs/base_env.py

     dimension_property: Tuple[DimensionProperty, ...]
     observation_type: ObservationType
 
-    # Optional name. For observations coming from com.unity.ml-agents, this
-    # will be the ISensor name.
-    name: str
-
 
 class BehaviorSpec(NamedTuple):
     """

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\"\x8f\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\x12\x0c\n\x04name\x18\x08 \x01(\t\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')
+  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=480,
-  serialized_end=521,
+  serialized_start=466,
+  serialized_end=507,
 )
 _sym_db.RegisterEnumDescriptor(_COMPRESSIONTYPEPROTO)
 
   ],
   containing_type=None,
   options=None,
-  serialized_start=523,
-  serialized_end=593,
+  serialized_start=509,
+  serialized_end=579,
 )
 _sym_db.RegisterEnumDescriptor(_OBSERVATIONTYPEPROTO)
 
   extension_ranges=[],
   oneofs=[
   ],
-  serialized_start=433,
-  serialized_end=458,
+  serialized_start=419,
+  serialized_end=444,
 )
 
 _OBSERVATIONPROTO = _descriptor.Descriptor(
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
       options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='name', full_name='communicator_objects.ObservationProto.name', index=7,
-      number=8, type=9, cpp_type=9, label=1,
-      has_default_value=False, default_value=_b("").decode('utf-8'),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
   ],
   extensions=[
   ],
       index=0, containing_type=None, fields=[]),
   ],
   serialized_start=79,
-  serialized_end=478,
+  serialized_end=464,
 )
 
 _OBSERVATIONPROTO_FLOATDATA.containing_type = _OBSERVATIONPROTO

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

     Iterable as typing___Iterable,
     List as typing___List,
     Optional as typing___Optional,
-    Text as typing___Text,
     Tuple as typing___Tuple,
     cast as typing___cast,
 )
     compressed_channel_mapping = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___int]
     dimension_properties = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___int]
     observation_type = ... # type: ObservationTypeProto
-    name = ... # type: typing___Text
 
     @property
     def float_data(self) -> ObservationProto.FloatData: ...
         compressed_channel_mapping : typing___Optional[typing___Iterable[builtin___int]] = None,
         dimension_properties : typing___Optional[typing___Iterable[builtin___int]] = None,
         observation_type : typing___Optional[ObservationTypeProto] = None,
-        name : typing___Optional[typing___Text] = None,
         ) -> None: ...
     @classmethod
     def FromString(cls, s: builtin___bytes) -> ObservationProto: ...
         def HasField(self, field_name: typing_extensions___Literal[u"compressed_data",u"float_data",u"observation_data"]) -> builtin___bool: ...
-        def ClearField(self, field_name: typing_extensions___Literal[u"compressed_channel_mapping",u"compressed_data",u"compression_type",u"dimension_properties",u"float_data",u"name",u"observation_data",u"observation_type",u"shape"]) -> None: ...
+        def ClearField(self, field_name: typing_extensions___Literal[u"compressed_channel_mapping",u"compressed_data",u"compression_type",u"dimension_properties",u"float_data",u"observation_data",u"observation_type",u"shape"]) -> None: ...
-        def ClearField(self, field_name: typing_extensions___Literal[u"compressed_channel_mapping",b"compressed_channel_mapping",u"compressed_data",b"compressed_data",u"compression_type",b"compression_type",u"dimension_properties",b"dimension_properties",u"float_data",b"float_data",u"name",b"name",u"observation_data",b"observation_data",u"observation_type",b"observation_type",u"shape",b"shape"]) -> None: ...
+        def ClearField(self, field_name: typing_extensions___Literal[u"compressed_channel_mapping",b"compressed_channel_mapping",u"compressed_data",b"compressed_data",u"compression_type",b"compression_type",u"dimension_properties",b"dimension_properties",u"float_data",b"float_data",u"observation_data",b"observation_data",u"observation_type",b"observation_type",u"shape",b"shape"]) -> None: ...
     def WhichOneof(self, oneof_group: typing_extensions___Literal[u"observation_data",b"observation_data"]) -> typing_extensions___Literal["compressed_data","float_data"]: ...

ml-agents-envs/mlagents_envs/rpc_utils.py

     for obs in agent_info.observations:
         observation_specs.append(
             ObservationSpec(
-                name=obs.name,
-                shape=tuple(obs.shape),
-                observation_type=ObservationType(obs.observation_type),
-                dimension_property=tuple(
-                    DimensionProperty(dim) for dim in obs.dimension_properties
-                )
+                tuple(obs.shape),
+                tuple(DimensionProperty(dim) for dim in obs.dimension_properties)
+                ObservationType(obs.observation_type),
             )
         )
 
     return img
 
 
-def _check_observations_match_spec(
-    obs_index: int,
-    observation_spec: ObservationSpec,
-    agent_info_list: Collection[AgentInfoProto],
-) -> None:
-    """
-    Check that all the observations match the expected size.
-    This gives a nicer error than a cryptic numpy error later.
-    """
-    expected_obs_shape = tuple(observation_spec.shape)
-    for agent_info in agent_info_list:
-        agent_obs_shape = tuple(agent_info.observations[obs_index].shape)
-        if expected_obs_shape != agent_obs_shape:
-            raise UnityObservationException(
-                f"Observation at index={obs_index} for agent with "
-                f"id={agent_info.id} didn't match the ObservationSpec. "
-                f"Expected shape {expected_obs_shape} but got {agent_obs_shape}."
-            )
-
-
 @timed
 def _observation_to_np_array(
     obs: ObservationProto, expected_shape: Optional[Iterable[int]] = None
 @timed
 def _process_maybe_compressed_observation(
     obs_index: int,
-    observation_spec: ObservationSpec,
+    shape: Tuple[int, int, int],
-    shape = cast(Tuple[int, int, int], observation_spec.shape)
-    try:
-        batched_visual = [
-            _observation_to_np_array(agent_obs.observations[obs_index], shape)
-            for agent_obs in agent_info_list
-        ]
-    except ValueError:
-        # Try to get a more useful error message
-        _check_observations_match_spec(obs_index, observation_spec, agent_info_list)
-        # If that didn't raise anything, raise the original error
-        raise
+    batched_visual = [
+        _observation_to_np_array(agent_obs.observations[obs_index], shape)
+        for agent_obs in agent_info_list
+    ]
     return np.array(batched_visual, dtype=np.float32)
 
 
 
 @timed
 def _process_rank_one_or_two_observation(
-    obs_index: int,
-    observation_spec: ObservationSpec,
-    agent_info_list: Collection[AgentInfoProto],
+    obs_index: int, shape: Tuple[int, ...], agent_info_list: Collection[AgentInfoProto]
-        return np.zeros((0,) + observation_spec.shape, dtype=np.float32)
-    try:
-        np_obs = np.array(
-            [
-                agent_obs.observations[obs_index].float_data.data
-                for agent_obs in agent_info_list
-            ],
-            dtype=np.float32,
-        ).reshape((len(agent_info_list),) + observation_spec.shape)
-    except ValueError:
-        # Try to get a more useful error message
-        _check_observations_match_spec(obs_index, observation_spec, agent_info_list)
-        # If that didn't raise anything, raise the original error
-        raise
+        return np.zeros((0,) + shape, dtype=np.float32)
+    np_obs = np.array(
+        [
+            agent_obs.observations[obs_index].float_data.data
+            for agent_obs in agent_info_list
+        ],
+        dtype=np.float32,
+    ).reshape((len(agent_info_list),) + shape)
     _raise_on_nan_and_inf(np_obs, "observations")
     return np_obs
 
     ]
     decision_obs_list: List[np.ndarray] = []
     terminal_obs_list: List[np.ndarray] = []
-    for obs_index, observation_spec in enumerate(behavior_spec.observation_specs):
-        is_visual = len(observation_spec.shape) == 3
+    for obs_index, observation_specs in enumerate(behavior_spec.observation_specs):
+        is_visual = len(observation_specs.shape) == 3
+            obs_shape = cast(Tuple[int, int, int], observation_specs.shape)
-                    obs_index, observation_spec, decision_agent_info_list
+                    obs_index, obs_shape, decision_agent_info_list
-                    obs_index, observation_spec, terminal_agent_info_list
+                    obs_index, obs_shape, terminal_agent_info_list
-                    obs_index, observation_spec, decision_agent_info_list
+                    obs_index, observation_specs.shape, decision_agent_info_list
-                    obs_index, observation_spec, terminal_agent_info_list
+                    obs_index, observation_specs.shape, terminal_agent_info_list
                 )
             )
     decision_rewards = np.array(

ml-agents-envs/mlagents_envs/tests/test_rpc_utils.py

 def test_vector_observation():
     n_agents = 10
     shapes = [(3,), (4,)]
-    obs_specs = create_observation_specs_with_shapes(shapes)
-        arr = _process_rank_one_or_two_observation(
-            obs_index, obs_specs[obs_index], list_proto
-        )
+        arr = _process_rank_one_or_two_observation(obs_index, shape, list_proto)
-    shape = (128, 64, 3)
-    in_array_1 = np.random.rand(*shape)
+    in_array_1 = np.random.rand(128, 64, 3)
-    in_array_2 = np.random.rand(*shape)
+    in_array_2 = np.random.rand(128, 64, 3)
     in_array_2_mapping = [0, 1, 2]
     proto_obs_2 = generate_compressed_proto_obs_with_mapping(
         in_array_2, in_array_2_mapping
     ap2 = AgentInfoProto()
     ap2.observations.extend([proto_obs_2])
     ap_list = [ap1, ap2]
-    obs_spec = create_observation_specs_with_shapes([shape])[0]
-    arr = _process_maybe_compressed_observation(0, obs_spec, ap_list)
+    arr = _process_maybe_compressed_observation(0, (128, 64, 3), ap_list)
     assert list(arr.shape) == [2, 128, 64, 3]
     assert np.allclose(arr[0, :, :, :], in_array_1, atol=0.01)
     assert np.allclose(arr[1, :, :, :], in_array_2, atol=0.01)
     ap2 = AgentInfoProto()
     ap2.observations.extend([proto_obs_2])
     ap_list = [ap1, ap2]
-    shape = (128, 64, 1)
-    obs_spec = create_observation_specs_with_shapes([shape])[0]
-    arr = _process_maybe_compressed_observation(0, obs_spec, ap_list)
+    arr = _process_maybe_compressed_observation(0, (128, 64, 1), ap_list)
     assert list(arr.shape) == [2, 128, 64, 1]
     assert np.allclose(arr[0, :, :, :], expected_out_array_1, atol=0.01)
     assert np.allclose(arr[1, :, :, :], expected_out_array_2, atol=0.01)
     ap1 = AgentInfoProto()
     ap1.observations.extend([proto_obs_1])
     ap_list = [ap1]
-    shape = (128, 64, 8)
-    obs_spec = create_observation_specs_with_shapes([shape])[0]
-
-    arr = _process_maybe_compressed_observation(0, obs_spec, ap_list)
+    arr = _process_maybe_compressed_observation(0, (128, 64, 8), ap_list)
     assert list(arr.shape) == [1, 128, 64, 8]
     assert np.allclose(arr[0, :, :, :], expected_out_array_1, atol=0.01)
 
     ap1 = AgentInfoProto()
     ap1.observations.extend([proto_obs_1])
     ap_list = [ap1]
-
-    shape = (128, 42, 3)
-    obs_spec = create_observation_specs_with_shapes([shape])[0]
-
-        _process_maybe_compressed_observation(0, obs_spec, ap_list)
+        _process_maybe_compressed_observation(0, (128, 42, 3), ap_list)
 
 
 def test_batched_step_result_from_proto():
     assert decision_steps.obs[1].shape[1] == shapes[1][0]
     assert terminal_steps.obs[0].shape[1] == shapes[0][0]
     assert terminal_steps.obs[1].shape[1] == shapes[1][0]
-
-
-def test_mismatch_observations_raise_in_step_result_from_proto():
-    n_agents = 10
-    shapes = [(3,), (4,)]
-    spec = BehaviorSpec(
-        create_observation_specs_with_shapes(shapes), ActionSpec.create_continuous(3)
-    )
-    ap_list = generate_list_agent_proto(n_agents, shapes)
-    # Hack an observation to be larger, we should get an exception
-    ap_list[0].observations[0].shape[0] += 1
-    ap_list[0].observations[0].float_data.data.append(0.42)
-    with pytest.raises(UnityObservationException):
-        steps_from_proto(ap_list, spec)
 
 
 def test_action_masking_discrete():

ml-agents/README.md

 Install the `mlagents` package with:
 
 ```sh
-python -m pip install mlagents==0.24.1
+pip3 install mlagents
 ```
 
 ## Usage & More Information

ml-agents/mlagents/trainers/buffer.py

         else:
             return return_data
 
-    @property
-    def contains_lists(self) -> bool:
-        """
-        Checks whether this AgentBufferField contains List[np.ndarray].
-        """
-        return len(self) > 0 and isinstance(self[0], list)
-
-    def append(self, element: BufferEntry, padding_value: float = 0.0) -> None:
+    def append(self, element: np.ndarray, padding_value: float = 0.0) -> None:
         """
         Adds an element to this list. Also lets you change the padding
         type, so that it can be set on append (e.g. action_masks should
                     " too large given the current number of data points."
                 )
             if batch_size * training_length > len(self):
-                if self.contains_lists:
-                    padding = []
-                else:
-                    # We want to duplicate the last value in the array, multiplied by the padding_value.
-                    padding = np.array(self[-1], dtype=np.float32) * self.padding_value
+                padding = np.array(self[-1], dtype=np.float32) * self.padding_value
                 return [padding] * (training_length - leftover) + self[:]
 
             else:
             dimension is equal to the length of the AgentBufferField.
         """
         if len(self) > 0 and not isinstance(self[0], list):
-            return np.asanyarray(self, dtype=dtype)
+            return np.asanyarray(self, dytpe=dtype)
 
         shape = None
         for _entry in self:

ml-agents/mlagents/trainers/optimizer/torch_optimizer.py

 
         # If we're using LSTM, we want to get all the intermediate memories.
         all_next_memories: Optional[AgentBufferField] = None
+        if self.policy.use_recurrent:
+            (
+                value_estimates,
+                all_next_memories,
+                next_memory,
+            ) = self._evaluate_by_sequence(current_obs, memory)
+        else:
+            value_estimates, next_memory = self.critic.critic_pass(
+                current_obs, memory, sequence_length=batch.num_experiences
+            )
-        # To prevent memory leak and improve performance, evaluate with no_grad.
-        with torch.no_grad():
-            if self.policy.use_recurrent:
-                (
-                    value_estimates,
-                    all_next_memories,
-                    next_memory,
-                ) = self._evaluate_by_sequence(current_obs, memory)
-            else:
-                value_estimates, next_memory = self.critic.critic_pass(
-                    current_obs, memory, sequence_length=batch.num_experiences
-                )
-
-        # Store the memory for the next trajectory. This should NOT have a gradient.
+        # Store the memory for the next trajectory
         self.critic_memory_dict[agent_id] = next_memory
 
         next_value_estimate, _ = self.critic.critic_pass(

ml-agents/mlagents/trainers/ppo/optimizer_torch.py

         return update_stats
 
     def get_modules(self):
-        modules = {
-            "Optimizer:value_optimizer": self.optimizer,
-            "Optimizer:critic": self._critic,
-        }
+        modules = {"Optimizer": self.optimizer}
         for reward_provider in self.reward_signals.values():
             modules.update(reward_provider.get_modules())
         return modules

ml-agents/mlagents/trainers/ppo/trainer.py

             self.policy.update_normalization(agent_buffer_trajectory)
 
         # Get all value estimates
-        (
-            value_estimates,
-            value_next,
-            value_memories,
-        ) = self.optimizer.get_trajectory_value_estimates(
+        value_estimates, value_next, value_memories = self.optimizer.get_trajectory_value_estimates(
             agent_buffer_trajectory,
             trajectory.next_obs,
             trajectory.done_reached and not trajectory.interrupted,
             int(self.hyperparameters.batch_size / self.policy.sequence_length), 1
         )
 
-        advantages = np.array(
-            self.update_buffer[BufferKey.ADVANTAGES].get_batch(), dtype=np.float32
-        )
+        advantages = np.array(self.update_buffer[BufferKey.ADVANTAGES].get_batch())
         self.update_buffer[BufferKey.ADVANTAGES].set(
             (advantages - advantages.mean()) / (advantages.std() + 1e-10)
         )

ml-agents/mlagents/trainers/sac/optimizer_torch.py

 
     def get_modules(self):
         modules = {
-            "Optimizer:q_network": self.q_network,
-            "Optimizer:value_network": self._critic,
+            "Optimizer:value_network": self.q_network,
             "Optimizer:target_network": self.target_network,
             "Optimizer:policy_optimizer": self.policy_optimizer,
             "Optimizer:value_optimizer": self.value_optimizer,

ml-agents/mlagents/trainers/tests/dummy_config.py

     shapes: List[Tuple[int, ...]]
 ) -> List[ObservationSpec]:
     obs_specs: List[ObservationSpec] = []
-    for i, shape in enumerate(shapes):
+    for shape in shapes:
-        spec = ObservationSpec(
-            name=f"observation {i} with shape {shape}",
-            shape=shape,
-            dimension_property=dim_prop,
-            observation_type=ObservationType.DEFAULT,
-        )
+        spec = ObservationSpec(shape, dim_prop, ObservationType.DEFAULT)
         obs_specs.append(spec)
     return obs_specs

ml-agents/mlagents/trainers/tests/simple_test_envs.py

         self.names = brain_names
         self.positions: Dict[str, List[float]] = {}
         self.step_count: Dict[str, float] = {}
-
-        # Concatenate the arguments for a consistent random seed
-        seed = (
-            brain_names,
-            step_size,
-            num_visual,
-            num_vector,
-            num_var_len,
-            vis_obs_size,
-            vec_obs_size,
-            var_len_obs_size,
-            action_sizes,
-        )
-        self.random = random.Random(str(seed))
-
+        self.random = random.Random(str(self.behavior_spec))
         self.goal: Dict[str, int] = {}
         self.action = {}
         self.rewards: Dict[str, float] = {}