Merge branch 'master' into asymm-envs

.circleci/config.yml

       directory:
         type: string
         description: Local directory to use for publishing (e.g. ml-agents)
+      username:
+        type: string
+        description: pypi username
+        default: mlagents
+      test_repository_args:
+        type: string
+        description: Optional override repository URL. Only use for tests, leave blank for "real"
+        default: ""
     docker:
       - image: circleci/python:3.6
     steps:
           command: |
             . venv/bin/activate
             cd << parameters.directory >>
-            twine upload -u mlagents -p $PYPI_PASSWORD dist/*
+            twine upload -u << parameters.username >> -p $PYPI_PASSWORD << parameters.test_repository_args >> dist/*
 
 workflows:
   version: 2
           pip_constraints: test_constraints_max_tf2_version.txt
       - markdown_link_check
       - pre-commit
+      # The first deploy jobs are the "real" ones that upload to pypi
-              only: /[0-9]+(\.[0-9]+)*(\.dev[0-9]+)*/
+              # Matches e.g. "release_123"
+              only: /^release_[0-9]+$/
             branches:
               ignore: /.*/
       - deploy:
             tags:
-              only: /[0-9]+(\.[0-9]+)*(\.dev[0-9]+)*/
+              # Matches e.g. "release_123"
+              only: /^release_[0-9]+$/
             branches:
               ignore: /.*/
       - deploy:
             tags:
-              only: /[0-9]+(\.[0-9]+)*(\.dev[0-9]+)*/
+              # Matches e.g. "release_123"
+              only: /^release_[0-9]+$/
+            branches:
+              ignore: /.*/
+      # These deploy jobs upload to the pypi test repo. They have different tag triggers than the real ones.
+      - deploy:
+          name: test deploy ml-agents-envs
+          directory: ml-agents-envs
+          username: mlagents-test
+          test_repository_args: --repository-url https://test.pypi.org/legacy/
+          filters:
+            tags:
+              # Matches e.g. "release_123_test456
+              only: /^release_[0-9]+_test[0-9]+$/
+            branches:
+              ignore: /.*/
+      - deploy:
+          name: test deploy ml-agents
+          directory: ml-agents
+          username: mlagents-test
+          test_repository_args: --repository-url https://test.pypi.org/legacy/
+          filters:
+            tags:
+              # Matches e.g. "release_123_test456
+              only: /^release_[0-9]+_test[0-9]+$/
+            branches:
+              ignore: /.*/
+      - deploy:
+          name: test deploy gym-unity
+          directory: gym-unity
+          username: mlagents-test
+          test_repository_args: --repository-url https://test.pypi.org/legacy/
+          filters:
+            tags:
+              # Matches e.g. "release_123_test456
+              only: /^release_[0-9]+_test[0-9]+$/
             branches:
               ignore: /.*/
   nightly:

.gitignore

-# Tensorflow Model Info
+# Output Artifacts (Legacy)
+# Output Artifacts
+/results
 
 # Training environments
 /envs

.pylintrc

 
     # Using the global statement
     W0603,
+
+    # "Access to a protected member _foo of a client class (protected-access)"
+    W0212

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

 using UnityEngine;
 using UnityEditor.Build.Reporting;
 
-namespace MLAgents
+namespace Unity.MLAgents
-        const string k_outputCommandLineFlag = "--mlagents-build-output-path";
-        const string k_sceneCommandLineFlag = "--mlagents-build-scene-path";
+        const string k_OutputCommandLineFlag = "--mlagents-build-output-path";
+        const string k_SceneCommandLineFlag = "--mlagents-build-scene-path";
 
         public static void BuildStandalonePlayerOSX()
         {
             var args = Environment.GetCommandLineArgs();
             for (var i = 0; i < args.Length - 1; i++)
             {
-                if (args[i] == k_outputCommandLineFlag)
+                if (args[i] == k_OutputCommandLineFlag)
-                else if (args[i] == k_sceneCommandLineFlag)
+                else if (args[i] == k_SceneCommandLineFlag)
                 {
                     scenePath = args[i + 1];
                 }

Project/Assets/ML-Agents/Examples/3DBall/Demos/Expert3DBall.demo.meta

   fileIDToRecycleName:
     11400000: Assets/ML-Agents/Examples/3DBall/Demos/Expert3DBall.demo
   externalObjects: {}
-  userData: ' (MLAgents.Demonstrations.DemonstrationSummary)'
+  userData: ' (Unity.MLAgents.Demonstrations.DemonstrationSummary)'
   assetBundleName: 
   assetBundleVariant: 
   script: {fileID: 11500000, guid: 7bd65ce151aaa4a41a45312543c56be1, type: 3}

Project/Assets/ML-Agents/Examples/3DBall/Demos/Expert3DBallHard.demo.meta

   fileIDToRecycleName:
     11400000: Assets/ML-Agents/Examples/3DBall/Demos/Expert3DBallHard.demo
   externalObjects: {}
-  userData: ' (MLAgents.Demonstrations.DemonstrationSummary)'
+  userData: ' (Unity.MLAgents.Demonstrations.DemonstrationSummary)'
   assetBundleName: 
   assetBundleVariant: 
   script: {fileID: 11500000, guid: 7bd65ce151aaa4a41a45312543c56be1, type: 3}

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

 using UnityEngine;
-using MLAgents;
-using MLAgents.Sensors;
-using MLAgents.SideChannels;
+using Unity.MLAgents;
+using Unity.MLAgents.Sensors;
 
 public class Ball3DAgent : Agent
 {

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

 using UnityEngine;
-using MLAgents;
-using MLAgents.Sensors;
-using MLAgents.SideChannels;
+using Unity.MLAgents;
+using Unity.MLAgents.Sensors;
 
 public class Ball3DHardAgent : Agent
 {

Project/Assets/ML-Agents/Examples/Basic/Demos/ExpertBasic.demo.meta

   fileIDToRecycleName:
     11400000: Assets/ML-Agents/Examples/Basic/Demos/ExpertBasic.demo
   externalObjects: {}
-  userData: ' (MLAgents.Demonstrations.DemonstrationSummary)'
+  userData: ' (Unity.MLAgents.Demonstrations.DemonstrationSummary)'
   assetBundleName: 
   assetBundleVariant: 
   script: {fileID: 11500000, guid: 7bd65ce151aaa4a41a45312543c56be1, type: 3}

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

 using UnityEngine;
 using UnityEngine.SceneManagement;
-using MLAgents;
+using Unity.MLAgents;
+using UnityEngine.Serialization;
 
 /// <summary>
 /// An example of how to use ML-Agents without inheriting from the Agent class.
 {
     public float timeBetweenDecisionsAtInference;
     float m_TimeSinceDecision;
+    [FormerlySerializedAs("m_Position")]
-    public int m_Position;
+    public int position;
     const int k_SmallGoalPosition = 7;
     const int k_LargeGoalPosition = 17;
     public GameObject largeGoal;
     public void OnEnable()
     {
         m_Agent = GetComponent<Agent>();
-        m_Position = 10;
-        transform.position = new Vector3(m_Position - 10f, 0f, 0f);
+        position = 10;
+        transform.position = new Vector3(position - 10f, 0f, 0f);
         smallGoal.transform.position = new Vector3(k_SmallGoalPosition - 10f, 0f, 0f);
         largeGoal.transform.position = new Vector3(k_LargeGoalPosition - 10f, 0f, 0f);
     }
                 break;
         }
 
-        m_Position += direction;
-        if (m_Position < k_MinPosition) { m_Position = k_MinPosition; }
-        if (m_Position > k_MaxPosition) { m_Position = k_MaxPosition; }
+        position += direction;
+        if (position < k_MinPosition) { position = k_MinPosition; }
+        if (position > k_MaxPosition) { position = k_MaxPosition; }
-        gameObject.transform.position = new Vector3(m_Position - 10f, 0f, 0f);
+        gameObject.transform.position = new Vector3(position - 10f, 0f, 0f);
-        if (m_Position == k_SmallGoalPosition)
+        if (position == k_SmallGoalPosition)
         {
             m_Agent.AddReward(0.1f);
             m_Agent.EndEpisode();
-        if (m_Position == k_LargeGoalPosition)
+        if (position == k_LargeGoalPosition)
         {
             m_Agent.AddReward(1f);
             m_Agent.EndEpisode();
 
     public void ResetAgent()
-    {   
+    {
         // This is a very inefficient way to reset the scene. Used here for testing.
         SceneManager.LoadScene(SceneManager.GetActiveScene().name);
         m_Agent = null; // LoadScene only takes effect at the next Update.

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

 using System;
-using MLAgents.Sensors;
-using UnityEngine.Serialization;
+using Unity.MLAgents.Sensors;
-namespace MLAgentsExamples
+namespace Unity.MLAgentsExamples
 {
     /// <summary>
     /// A simple example of a SensorComponent.
         {
             // One-hot encoding of the position
             Array.Clear(output, 0, output.Length);
-            output[basicController.m_Position] = 1;
+            output[basicController.position] = 1;
         }
 
         /// <inheritdoc/>

Project/Assets/ML-Agents/Examples/Basic/TFModels/Basic.nn

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>}���%�Ͼ�Ɖ>�ܘ�N��ӶG<f1��d�?0�K>�꾀"k��Ϭ>���>^�?��'�+�e(��s��c��>7���J����&����>��e�D�(=]����q�rJ�>.y>�(����eϪ>�(>;�,==5�/�>S%>2}>n#n=�C=�i>y���d��=��ν�|�8[)���0�=L��
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p�<3ś=�5^���>��=�D->��)>)�˽g����s?H���ε�xc���Ҿ �M?hRӾ�D�S��P*,?�Ծ���f#�K��mC&?�(@?Pꬾ�ാ�x�>Z�?J��>��پk���s�u��&�����>] ��鸉���"���@?-r��Ҝ�x���F}���5�>�T�>��+�����(.?���>U���4n�=�w��`�*�$5>q6�>O�Z�=��=�k�=�L�<&q= �u>d#
+=M���&����ꖾy:;>��²{>6ƀ<:�h�G^�������T���1�>N��[�s�� =�:!>o{>4���A䛾lo�� �]>{�v�`n�c=4���f?�>M���0
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���~�=HO�>N8B��Ui�K	�=u��=e`?��+�ǟ;���.�?�)�DP\?�-���@�;-�9V\?N�<��\2��5�ؗ'���e?��W?bl+�\�/��5[?��]?�O����A?L?^?�;E�'�
?n�>{�8��)	?��
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+?ݪ5��?��?��?���
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Project/Assets/ML-Agents/Examples/Bouncer/Demos/ExpertBouncer.demo.meta

   fileIDToRecycleName:
     11400000: Assets/ML-Agents/Examples/Bouncer/Demos/ExpertBouncer.demo
   externalObjects: {}
-  userData: ' (MLAgents.Demonstrations.DemonstrationSummary)'
+  userData: ' (Unity.MLAgents.Demonstrations.DemonstrationSummary)'
   assetBundleName: 
   assetBundleVariant: 
   script: {fileID: 11500000, guid: 7bd65ce151aaa4a41a45312543c56be1, type: 3}

Project/Assets/ML-Agents/Examples/Bouncer/Scripts/BouncerAgent.cs

 using UnityEngine;
-using MLAgents;
-using MLAgents.Sensors;
-using MLAgents.SideChannels;
+using Unity.MLAgents;
+using Unity.MLAgents.Sensors;
 
 public class BouncerAgent : Agent
 {

Project/Assets/ML-Agents/Examples/Bouncer/Scripts/BouncerTarget.cs

 using UnityEngine;
-using MLAgents;
+using Unity.MLAgents;
 
 public class BouncerTarget : MonoBehaviour
 {

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

   fileIDToRecycleName:
     11400000: Assets/ML-Agents/Examples/Crawler/Demos/ExpertCrawlerDyn.demo
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Project/Assets/ML-Agents/Examples/Crawler/Demos/ExpertCrawlerSta.demo.meta

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     11400000: Assets/ML-Agents/Examples/Crawler/Demos/ExpertCrawlerSta.demo
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Project/Assets/ML-Agents/Examples/Crawler/Scripts/CrawlerAgent.cs

 using UnityEngine;
-using MLAgents;
-using MLAgentsExamples;
-using MLAgents.Sensors;
+using Unity.MLAgents;
+using Unity.MLAgentsExamples;
+using Unity.MLAgents.Sensors;
 
 [RequireComponent(typeof(JointDriveController))] // Required to set joint forces
 public class CrawlerAgent : Agent

Project/Assets/ML-Agents/Examples/FoodCollector/Demos/ExpertFood.demo.meta

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     11400000: Assets/ML-Agents/Examples/FoodCollector/Demos/ExpertFood.demo
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Project/Assets/ML-Agents/Examples/FoodCollector/Scripts/FoodCollectorAgent.cs

 using UnityEngine;
-using MLAgents;
-using MLAgents.Sensors;
-using MLAgents.SideChannels;
+using Unity.MLAgents;
+using Unity.MLAgents.Sensors;
 
 public class FoodCollectorAgent : Agent
 {

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

 using UnityEngine;
-using MLAgentsExamples;
+using Unity.MLAgentsExamples;
 
 public class FoodCollectorArea : Area
 {

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

 using UnityEngine;
 using UnityEngine.UI;
-using MLAgents;
+using Unity.MLAgents;
 
 public class FoodCollectorSettings : MonoBehaviour
 {
         m_Recorder = Academy.Instance.StatsRecorder;
     }
 
-    private void EnvironmentReset()
+    void EnvironmentReset()
     {
         ClearObjects(GameObject.FindGameObjectsWithTag("food"));
         ClearObjects(GameObject.FindGameObjectsWithTag("badFood"));

Project/Assets/ML-Agents/Examples/GridWorld/Demos/ExpertGrid.demo.meta

 fileFormatVersion: 2
-guid: 3938e0ee1f99e473db8e45d334dfa329
+guid: 0092f2e4aece345aea4730a37eeebf68
-  userData: ' (MLAgents.Demonstrations.DemonstrationSummary)'
+  userData: ' (Unity.MLAgents.Demonstrations.DemonstrationSummary)'
   assetBundleName: 
   assetBundleVariant: 
   script: {fileID: 11500000, guid: 7bd65ce151aaa4a41a45312543c56be1, type: 3}

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

 using System;
 using UnityEngine;
 using System.Linq;
-using MLAgents;
-using MLAgents.Sensors;
+using Unity.MLAgents;
-using MLAgents.SideChannels;
 
 public class GridAgent : Agent
 {
 
             if (positionX == 0)
             {
-                actionMasker.SetMask(0, new int[]{ k_Left});
+                actionMasker.SetMask(0, new []{ k_Left});
-                actionMasker.SetMask(0, new int[]{k_Right});
+                actionMasker.SetMask(0, new []{k_Right});
-                actionMasker.SetMask(0, new int[]{k_Down});
+                actionMasker.SetMask(0, new []{k_Down});
-                actionMasker.SetMask(0, new int[]{k_Up});
+                actionMasker.SetMask(0, new []{k_Up});
             }
         }
     }

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

 using System.Collections.Generic;
 using UnityEngine;
 using System.Linq;
-using MLAgents;
-using MLAgents.SideChannels;
+using Unity.MLAgents;
 
 
 public class GridArea : MonoBehaviour
         m_InitialPosition = transform.position;
     }
 
-    private void SetEnvironment()
+    void SetEnvironment()
     {
         transform.position = m_InitialPosition * (m_ResetParams.GetWithDefault("gridSize", 5f) + 1);
         var playersList = new List<int>();

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

 using UnityEngine;
-using MLAgents;
-using MLAgents.SideChannels;
+using Unity.MLAgents;
 
 public class GridSettings : MonoBehaviour
 {

Project/Assets/ML-Agents/Examples/Hallway/Demos/ExpertHallway.demo.meta

   fileIDToRecycleName:
     11400000: Assets/ML-Agents/Examples/Hallway/Demos/ExpertHallway.demo
   externalObjects: {}
-  userData: ' (MLAgents.Demonstrations.DemonstrationSummary)'
+  userData: ' (Unity.MLAgents.Demonstrations.DemonstrationSummary)'
   assetBundleName: 
   assetBundleVariant: 
   script: {fileID: 11500000, guid: 7bd65ce151aaa4a41a45312543c56be1, type: 3}

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

 using System.Collections;
 using UnityEngine;
-using MLAgents;
-using MLAgents.Sensors;
+using Unity.MLAgents;
+using Unity.MLAgents.Sensors;
 
 public class HallwayAgent : Agent
 {

Project/Assets/ML-Agents/Examples/PushBlock/Demos/ExpertPush.demo.meta

   fileIDToRecycleName:
     11400000: Assets/ML-Agents/Examples/PushBlock/Demos/ExpertPush.demo
   externalObjects: {}
-  userData: ' (MLAgents.Demonstrations.DemonstrationSummary)'
+  userData: ' (Unity.MLAgents.Demonstrations.DemonstrationSummary)'
   assetBundleName: 
   assetBundleVariant: 
   script: {fileID: 11500000, guid: 7bd65ce151aaa4a41a45312543c56be1, type: 3}

Project/Assets/ML-Agents/Examples/PushBlock/Scripts/PushAgentBasic.cs

 
 using System.Collections;
 using UnityEngine;
-using MLAgents;
-using MLAgents.SideChannels;
+using Unity.MLAgents;
 
 public class PushAgentBasic : Agent
 {
     /// </summary>
     Renderer m_GroundRenderer;
 
-    private EnvironmentParameters m_ResetParams;
+    EnvironmentParameters m_ResetParams;
 
     void Awake()
     {
         m_BlockRb.drag = m_ResetParams.GetWithDefault("block_drag", 0.5f);
     }
 
-    private void SetResetParameters()
+    void SetResetParameters()
     {
         SetGroundMaterialFriction();
         SetBlockProperties();

Project/Assets/ML-Agents/Examples/Pyramids/Demos/ExpertPyramid.demo.meta

   fileIDToRecycleName:
     11400000: Assets/ML-Agents/Examples/Pyramids/Demos/ExpertPyramid.demo
   externalObjects: {}
-  userData: ' (MLAgents.Demonstrations.DemonstrationSummary)'
+  userData: ' (Unity.MLAgents.Demonstrations.DemonstrationSummary)'
   assetBundleName: 
   assetBundleVariant: 
   script: {fileID: 11500000, guid: 7bd65ce151aaa4a41a45312543c56be1, type: 3}

Project/Assets/ML-Agents/Examples/Pyramids/Scripts/PyramidAgent.cs

 using System.Linq;
 using UnityEngine;
 using Random = UnityEngine.Random;
-using MLAgents;
-using MLAgents.Sensors;
+using Unity.MLAgents;
+using Unity.MLAgents.Sensors;
 
 public class PyramidAgent : Agent
 {

Project/Assets/ML-Agents/Examples/Pyramids/Scripts/PyramidArea.cs

 using UnityEngine;
-using MLAgentsExamples;
+using Unity.MLAgentsExamples;
 
 public class PyramidArea : Area
 {

Project/Assets/ML-Agents/Examples/Reacher/Demos/ExpertReacher.demo.meta

   fileIDToRecycleName:
     11400000: Assets/ML-Agents/Examples/Reacher/Demos/ExpertReacher.demo
   externalObjects: {}
-  userData: ' (MLAgents.Demonstrations.DemonstrationSummary)'
+  userData: ' (Unity.MLAgents.Demonstrations.DemonstrationSummary)'
   assetBundleName: 
   assetBundleVariant: 
   script: {fileID: 11500000, guid: 7bd65ce151aaa4a41a45312543c56be1, type: 3}

Project/Assets/ML-Agents/Examples/Reacher/Scripts/ReacherAgent.cs

 using UnityEngine;
-using MLAgents;
-using MLAgents.Sensors;
-using MLAgents.SideChannels;
+using Unity.MLAgents;
+using Unity.MLAgents.Sensors;
 
 public class ReacherAgent : Agent
 {
     // Frequency of the cosine deviation of the goal along the vertical dimension
     float m_DeviationFreq;
 
-    private EnvironmentParameters m_ResetParams;
+    EnvironmentParameters m_ResetParams;
 
     /// <summary>
     /// Collect the rigidbodies of the reacher in order to resue them for

Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/Area.cs

 using UnityEngine;
 
-namespace MLAgentsExamples
+namespace Unity.MLAgentsExamples
 {
     public class Area : MonoBehaviour
     {

Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/CameraFollow.cs

 using UnityEngine;
 
-namespace MLAgentsExamples
+namespace Unity.MLAgentsExamples
 {
     public class CameraFollow : MonoBehaviour
     {

Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/FlyCamera.cs

 using UnityEngine;
 
-namespace MLAgentsExamples
+namespace Unity.MLAgentsExamples
 {
     public class FlyCamera : MonoBehaviour
     {

Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/GroundContact.cs

 using UnityEngine;
-using MLAgents;
+using Unity.MLAgents;
-namespace MLAgentsExamples
+namespace Unity.MLAgentsExamples
 {
     /// <summary>
     /// This class contains logic for locomotion agents with joints which might make contact with the ground.

Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/JointDriveController.cs

 using System.Collections.Generic;
 using UnityEngine;
 using UnityEngine.Serialization;
-using MLAgents;
+using Unity.MLAgents;
-namespace MLAgentsExamples
+namespace Unity.MLAgentsExamples
 {
     /// <summary>
     /// Used to store relevant information for acting and learning for each body part in agent.

Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/ModelOverrider.cs

 using UnityEngine;
 using Unity.Barracuda;
 using System.IO;
-using MLAgents;
-using MLAgents.Policies;
+using Unity.MLAgents;
+using Unity.MLAgents.Policies;
-namespace MLAgentsExamples
+namespace Unity.MLAgentsExamples
 {
     /// <summary>
     /// Utility class to allow the NNModel file for an agent to be overriden during inference.
         {
             m_Agent.LazyInitialize();
             var bp = m_Agent.GetComponent<BehaviorParameters>();
-            var name = bp.BehaviorName;
+            var behaviorName = bp.BehaviorName;
-            var nnModel = GetModelForBehaviorName(name);
-            Debug.Log($"Overriding behavior {name} for agent with model {nnModel?.name}");
+            var nnModel = GetModelForBehaviorName(behaviorName);
+            Debug.Log($"Overriding behavior {behaviorName} for agent with model {nnModel?.name}");
-            m_Agent.SetModel($"Override_{name}", nnModel);
+            m_Agent.SetModel($"Override_{behaviorName}", nnModel);
 
         }
     }

Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/Monitor.cs

 using System.Linq;
 using UnityEngine;
 
-namespace MLAgents
+namespace Unity.MLAgents
 {
     /// <summary>
     /// Monitor is used to display information about the Agent within the Unity
             s_TransformCamera = new Dictionary<Transform, Camera>();
         }
 
-        /// <inheritdoc/>
         void OnGUI()
         {
             if (!s_Initialized)

Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/ProjectSettingsOverrides.cs

 using UnityEngine;
-using MLAgents;
-using MLAgents.SideChannels;
+using Unity.MLAgents;
-namespace MLAgentsExamples
+namespace Unity.MLAgentsExamples
 {
     /// <summary>
     /// A helper class for the ML-Agents example scenes to override various

Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/SensorBase.cs

-using MLAgents.Sensors;
+using Unity.MLAgents.Sensors;
-namespace MLAgentsExamples
+namespace Unity.MLAgentsExamples
 {
     /// <summary>
     /// A simple sensor that provides a number default implementations.

Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/TargetContact.cs

 using UnityEngine;
 
-namespace MLAgentsExamples
+namespace Unity.MLAgentsExamples
 {
     /// <summary>
     /// This class contains logic for locomotion agents with joints which might make contact with a target.

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

 using System;
 using UnityEngine;
-using MLAgents;
-using MLAgents.Policies;
-using MLAgents.SideChannels;
+using Unity.MLAgents;
+using Unity.MLAgents.Policies;
 
 public class AgentSoccer : Agent
 {
     float m_ForwardSpeed;
 
     [HideInInspector]
-    public float timePenalty = 0;
+    public float timePenalty;
 
     [HideInInspector]
     public Rigidbody agentRb;
 
-    private EnvironmentParameters m_ResetParams;
+    EnvironmentParameters m_ResetParams;
 
     public override void Initialize()
     {

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

 using System.Collections;
 using System.Collections.Generic;
-using MLAgents;
-using MLAgents.SideChannels;
+using Unity.MLAgents;
 using UnityEngine;
 using UnityEngine.Serialization;
 
     [HideInInspector]
     public bool canResetBall;
 
-    private EnvironmentParameters m_ResetParams;
+    EnvironmentParameters m_ResetParams;
 
     void Awake()
     {

Project/Assets/ML-Agents/Examples/Startup/Scripts/Startup.cs

 using UnityEngine;
 using UnityEngine.SceneManagement;
 
-namespace MLAgentsExamples
+namespace Unity.MLAgentsExamples
-        private const string k_SceneCommandLineFlag = "--mlagents-scene-name";
+        const string k_SceneCommandLineFlag = "--mlagents-scene-name";
-            
+
             // Check for the CLI '--scene-name' flag.  This will be used if
             // no scene environment variable is found.
             var args = Environment.GetCommandLineArgs();
             {
                 sceneName = sceneEnvironmentVariable;
             }
-            
+
             SwitchScene(sceneName);
         }
 

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

 using UnityEngine;
-using MLAgents;
-using MLAgents.Sensors;
+using Unity.MLAgents;
+using Unity.MLAgents.Sensors;
 
 public class TemplateAgent : Agent
 {

Project/Assets/ML-Agents/Examples/Tennis/Demos/ExpertTennis.demo.meta

   fileIDToRecycleName:
     11400000: Assets/ML-Agents/Examples/Tennis/Demos/ExpertTennis.demo
   externalObjects: {}
-  userData: ' (MLAgents.Demonstrations.DemonstrationSummary)'
+  userData: ' (Unity.MLAgents.Demonstrations.DemonstrationSummary)'
   assetBundleName: 
   assetBundleVariant: 
   script: {fileID: 11500000, guid: 7bd65ce151aaa4a41a45312543c56be1, type: 3}

Project/Assets/ML-Agents/Examples/Tennis/Scripts/TennisAgent.cs

 using UnityEngine;
 using UnityEngine.UI;
-using MLAgents;
-using MLAgents.Sensors;
-using MLAgents.SideChannels;
+using Unity.MLAgents;
+using Unity.MLAgents.Sensors;
 
 public class TennisAgent : Agent
 {

Project/Assets/ML-Agents/Examples/Walker/Demos/ExpertWalker.demo.meta

   fileIDToRecycleName:
     11400000: Assets/ML-Agents/Examples/Walker/Demos/ExpertWalker.demo
   externalObjects: {}
-  userData: ' (MLAgents.Demonstrations.DemonstrationSummary)'
+  userData: ' (Unity.MLAgents.Demonstrations.DemonstrationSummary)'
   assetBundleName: 
   assetBundleVariant: 
   script: {fileID: 11500000, guid: 7bd65ce151aaa4a41a45312543c56be1, type: 3}

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

 using UnityEngine;
-using MLAgents;
-using MLAgentsExamples;
-using MLAgents.Sensors;
-using MLAgents.SideChannels;
+using Unity.MLAgents;
+using Unity.MLAgentsExamples;
+using Unity.MLAgents.Sensors;
 
 public class WalkerAgent : Agent
 {

Project/Assets/ML-Agents/Examples/WallJump/Scripts/WallJumpAgent.cs

 
 using System.Collections;
 using UnityEngine;
-using MLAgents;
+using Unity.MLAgents;
-using MLAgents.Sensors;
-using MLAgents.SideChannels;
+using Unity.MLAgents.Sensors;
 
 public class WallJumpAgent : Agent
 {

Project/Assets/ML-Agents/Examples/Worm/Scripts/WormAgent.cs

-using System.Collections;
-using MLAgents;
-using MLAgentsExamples;
-using MLAgents.Sensors;
+using Unity.MLAgents;
+using Unity.MLAgentsExamples;
+using Unity.MLAgents.Sensors;
 
 [RequireComponent(typeof(JointDriveController))] // Required to set joint forces
 public class WormAgent : Agent
     public bool respawnTargetWhenTouched;
     public float targetSpawnRadius;
 
-    [Header("Body Parts")] [Space(10)] 
+    [Header("Body Parts")] [Space(10)]
-    [Header("Joint Settings")] [Space(10)] 
+    [Header("Joint Settings")] [Space(10)]
     JointDriveController m_JdController;
     Vector3 m_DirToTarget;
     float m_MovingTowardsDot;
         m_JdController.SetupBodyPart(bodySegment1);
         m_JdController.SetupBodyPart(bodySegment2);
         m_JdController.SetupBodyPart(bodySegment3);
-        
+
         //We only want the head to detect the target
         //So we need to remove TargetContact from everything else
         //This is a temp fix till we can redesign
     }
 
-    
+
     //Get Joint Rotation Relative to the Connected Rigidbody
     //We want to collect this info because it is the actual rotation, not the "target rotation"
     public Quaternion GetJointRotation(ConfigurableJoint joint)
             sensor.AddObservation(bp.currentStrength / m_JdController.maxJointForceLimit);
         }
     }
-    
+
     public override void CollectObservations(VectorSensor sensor)
     {
         m_JdController.GetCurrentJointForces();

README.md

-<img src="docs/images/unity-wide.png" align="middle" width="3000"/>
+<img src="docs/images/image-banner.png" align="middle" width="3000"/>
+
+# Unity ML-Agents Toolkit
-<img src="docs/images/image-banner.png" align="middle" width="3000"/>
+[![docs badge](https://img.shields.io/badge/docs-reference-blue.svg)](https://github.com/Unity-Technologies/ml-agents/tree/release_1_docs/docs/)
-# Unity ML-Agents Toolkit (Beta)
-[![docs badge](https://img.shields.io/badge/docs-reference-blue.svg)](https://github.com/Unity-Technologies/ml-agents/tree/latest_release/docs/)
 [![license badge](https://img.shields.io/badge/license-Apache--2.0-green.svg)](LICENSE)
 
 ([latest release](https://github.com/Unity-Technologies/ml-agents/releases/tag/latest_release))
-Unity plugin that enables games and simulations to serve as environments for
+project that enables games and simulations to serve as environments for
 training intelligent agents. Agents can be trained using reinforcement learning,
 imitation learning, neuroevolution, or other machine learning methods through a
 simple-to-use Python API. We also provide implementations (based on TensorFlow)
 
 ## Features
 
-* Unity environment control from Python
-* 15+ sample Unity environments
-* Two deep reinforcement learning algorithms,
-[Proximal Policy Optimization](docs/Training-PPO.md)
- (PPO) and [Soft Actor-Critic](docs/Training-SAC.md)
- (SAC)
-* Support for multiple environment configurations and training scenarios
-* Self-play mechanism for training agents in adversarial scenarios
-* Train memory-enhanced agents using deep reinforcement learning
-* Easily definable Curriculum Learning and Generalization scenarios
-* Built-in support for [Imitation Learning](docs/Training-Imitation-Learning.md) through Behavioral Cloning or Generative Adversarial Imitation Learning
-* Flexible agent control with On Demand Decision Making
-* Visualizing network outputs within the environment
-* Wrap learning environments as a gym
-* Utilizes the Unity Inference Engine
-* Train using concurrent Unity environment instances
+- 15+ [example Unity environments](docs/Learning-Environment-Examples.md)
+- Support for multiple environment configurations and training scenarios
+- Flexible Unity SDK that can be integrated into your game or custom Unity scene
+- Training using two deep reinforcement learning algorithms, Proximal Policy
+  Optimization (PPO) and Soft Actor-Critic (SAC)
+- Built-in support for Imitation Learning through Behavioral Cloning or
+  Generative Adversarial Imitation Learning
+- Self-play mechanism for training agents in adversarial scenarios
+- Easily definable Curriculum Learning scenarios for complex tasks
+- Train robust agents using environment randomization
+- Flexible agent control with On Demand Decision Making
+- Train using multiple concurrent Unity environment instances
+- Utilizes the [Unity Inference Engine](docs/Unity-Inference-Engine.md) to
+  provide native cross-platform support
+- Unity environment [control from Python](docs/Python-API.md)
+- Wrap Unity learning environments as a [gym](gym-unity/README.md)
+
+See our [ML-Agents Overview](docs/ML-Agents-Overview.md) page for detailed
+descriptions of all these features.
-**Our latest, stable release is 0.15.1. Click
-[here](https://github.com/Unity-Technologies/ml-agents/tree/latest_release/docs/Readme.md) to
+
+**Our latest, stable release is `Release 1`. Click [here](docs/Readme.md) to
 get started with the latest release of ML-Agents.**
 
 The table below lists all our releases, including our `master` branch which is under active
 details of the changes between versions.
 * If you have used an earlier version of the ML-Agents Toolkit, we strongly recommend our
 [guide on migrating from earlier versions](docs/Migrating.md).
-
-| **0.15.1** | **March 30, 2020** | **[source](https://github.com/Unity-Technologies/ml-agents/tree/0.15.1)** | **[docs](https://github.com/Unity-Technologies/ml-agents/tree/0.15.1/docs/Readme.md)** | **[download](https://github.com/Unity-Technologies/ml-agents/archive/0.15.1.zip)** |
+| **Release 1** | **April 30, 2020** | **[source](https://github.com/Unity-Technologies/ml-agents/tree/release_1)** | **[docs](https://github.com/Unity-Technologies/ml-agents/tree/release_1/docs/Readme.md)** | **[download](https://github.com/Unity-Technologies/ml-agents/archive/release_1.zip)** |
+| **0.15.1** | March 30, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/0.15.1) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/0.15.1/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/0.15.1.zip) |
 | **0.15.0** | March 18, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/0.15.0) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/0.15.0/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/0.15.0.zip) |
 | **0.14.1** | February 26, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/0.14.1) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/0.14.1/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/0.14.1.zip) |
 | **0.14.0** | February 13, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/0.14.0) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/0.14.0/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/0.14.0.zip) |
 | **0.12.0** | December 2, 2019 | [source](https://github.com/Unity-Technologies/ml-agents/tree/0.12.0) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/0.12.0/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/0.12.0.zip) |
-| **0.11.0** | November 4, 2019 | [source](https://github.com/Unity-Technologies/ml-agents/tree/0.11.0) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/0.11.0/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/0.11.0.zip) |
-If you are a researcher interested in a discussion of Unity as an AI platform, see a pre-print
-of our [reference paper on Unity and the ML-Agents Toolkit](https://arxiv.org/abs/1809.02627).
+If you are a researcher interested in a discussion of Unity as an AI platform,
+see a pre-print of our
+[reference paper on Unity and the ML-Agents Toolkit](https://arxiv.org/abs/1809.02627).
-If you use Unity or the ML-Agents Toolkit to conduct research, we ask that you cite the following
-paper as a reference:
-
-Juliani, A., Berges, V., Vckay, E., Gao, Y., Henry, H., Mattar, M., Lange, D. (2018). Unity: A General Platform for Intelligent Agents. *arXiv preprint arXiv:1809.02627.* https://github.com/Unity-Technologies/ml-agents.
+If you use Unity or the ML-Agents Toolkit to conduct research, we ask that you
+cite the following paper as a reference:
+Juliani, A., Berges, V., Vckay, E., Gao, Y., Henry, H., Mattar, M., Lange, D.
+(2018). Unity: A General Platform for Intelligent Agents. _arXiv preprint
+arXiv:1809.02627._ https://github.com/Unity-Technologies/ml-agents.
-* (February 28, 2020) [Training intelligent adversaries using self-play with ML-Agents](https://blogs.unity3d.com/2020/02/28/training-intelligent-adversaries-using-self-play-with-ml-agents/)
-* (November 11, 2019) [Training your agents 7 times faster with ML-Agents](https://blogs.unity3d.com/2019/11/11/training-your-agents-7-times-faster-with-ml-agents/)
-* (October 21, 2019) [The AI@Unity interns help shape the world](https://blogs.unity3d.com/2019/10/21/the-aiunity-interns-help-shape-the-world/)
-* (April 15, 2019) [Unity ML-Agents Toolkit v0.8: Faster training on real games](https://blogs.unity3d.com/2019/04/15/unity-ml-agents-toolkit-v0-8-faster-training-on-real-games/)
-* (March 1, 2019) [Unity ML-Agents Toolkit v0.7: A leap towards cross-platform inference](https://blogs.unity3d.com/2019/03/01/unity-ml-agents-toolkit-v0-7-a-leap-towards-cross-platform-inference/)
-* (December 17, 2018) [ML-Agents Toolkit v0.6: Improved usability of Brains and Imitation Learning](https://blogs.unity3d.com/2018/12/17/ml-agents-toolkit-v0-6-improved-usability-of-brains-and-imitation-learning/)
-* (October 2, 2018) [Puppo, The Corgi: Cuteness Overload with the Unity ML-Agents Toolkit](https://blogs.unity3d.com/2018/10/02/puppo-the-corgi-cuteness-overload-with-the-unity-ml-agents-toolkit/)
-* (September 11, 2018) [ML-Agents Toolkit v0.5, new resources for AI researchers available now](https://blogs.unity3d.com/2018/09/11/ml-agents-toolkit-v0-5-new-resources-for-ai-researchers-available-now/)
-* (June 26, 2018) [Solving sparse-reward tasks with Curiosity](https://blogs.unity3d.com/2018/06/26/solving-sparse-reward-tasks-with-curiosity/)
-* (June 19, 2018) [Unity ML-Agents Toolkit v0.4 and Udacity Deep Reinforcement Learning Nanodegree](https://blogs.unity3d.com/2018/06/19/unity-ml-agents-toolkit-v0-4-and-udacity-deep-reinforcement-learning-nanodegree/)
-* (May 24, 2018) [Imitation Learning in Unity: The Workflow](https://blogs.unity3d.com/2018/05/24/imitation-learning-in-unity-the-workflow/)
-* (March 15, 2018) [ML-Agents Toolkit v0.3 Beta released: Imitation Learning, feedback-driven features, and more](https://blogs.unity3d.com/2018/03/15/ml-agents-v0-3-beta-released-imitation-learning-feedback-driven-features-and-more/)
-* (December 11, 2017) [Using Machine Learning Agents in a real game: a beginner’s guide](https://blogs.unity3d.com/2017/12/11/using-machine-learning-agents-in-a-real-game-a-beginners-guide/)
-* (December 8, 2017) [Introducing ML-Agents Toolkit v0.2: Curriculum Learning, new environments, and more](https://blogs.unity3d.com/2017/12/08/introducing-ml-agents-v0-2-curriculum-learning-new-environments-and-more/)
-* (September 19, 2017) [Introducing: Unity Machine Learning Agents Toolkit](https://blogs.unity3d.com/2017/09/19/introducing-unity-machine-learning-agents/)
-* Overviewing reinforcement learning concepts
+
+- (February 28, 2020)
+  [Training intelligent adversaries using self-play with ML-Agents](https://blogs.unity3d.com/2020/02/28/training-intelligent-adversaries-using-self-play-with-ml-agents/)
+- (November 11, 2019)
+  [Training your agents 7 times faster with ML-Agents](https://blogs.unity3d.com/2019/11/11/training-your-agents-7-times-faster-with-ml-agents/)
+- (October 21, 2019)
+  [The AI@Unity interns help shape the world](https://blogs.unity3d.com/2019/10/21/the-aiunity-interns-help-shape-the-world/)
+- (April 15, 2019)
+  [Unity ML-Agents Toolkit v0.8: Faster training on real games](https://blogs.unity3d.com/2019/04/15/unity-ml-agents-toolkit-v0-8-faster-training-on-real-games/)
+- (March 1, 2019)
+  [Unity ML-Agents Toolkit v0.7: A leap towards cross-platform inference](https://blogs.unity3d.com/2019/03/01/unity-ml-agents-toolkit-v0-7-a-leap-towards-cross-platform-inference/)
+- (December 17, 2018)
+  [ML-Agents Toolkit v0.6: Improved usability of Brains and Imitation Learning](https://blogs.unity3d.com/2018/12/17/ml-agents-toolkit-v0-6-improved-usability-of-brains-and-imitation-learning/)
+- (October 2, 2018)
+  [Puppo, The Corgi: Cuteness Overload with the Unity ML-Agents Toolkit](https://blogs.unity3d.com/2018/10/02/puppo-the-corgi-cuteness-overload-with-the-unity-ml-agents-toolkit/)
+- (September 11, 2018)
+  [ML-Agents Toolkit v0.5, new resources for AI researchers available now](https://blogs.unity3d.com/2018/09/11/ml-agents-toolkit-v0-5-new-resources-for-ai-researchers-available-now/)
+- (June 26, 2018)
+  [Solving sparse-reward tasks with Curiosity](https://blogs.unity3d.com/2018/06/26/solving-sparse-reward-tasks-with-curiosity/)
+- (June 19, 2018)
+  [Unity ML-Agents Toolkit v0.4 and Udacity Deep Reinforcement Learning Nanodegree](https://blogs.unity3d.com/2018/06/19/unity-ml-agents-toolkit-v0-4-and-udacity-deep-reinforcement-learning-nanodegree/)
+- (May 24, 2018)
+  [Imitation Learning in Unity: The Workflow](https://blogs.unity3d.com/2018/05/24/imitation-learning-in-unity-the-workflow/)
+- (March 15, 2018)
+  [ML-Agents Toolkit v0.3 Beta released: Imitation Learning, feedback-driven features, and more](https://blogs.unity3d.com/2018/03/15/ml-agents-v0-3-beta-released-imitation-learning-feedback-driven-features-and-more/)
+- (December 11, 2017)
+  [Using Machine Learning Agents in a real game: a beginner’s guide](https://blogs.unity3d.com/2017/12/11/using-machine-learning-agents-in-a-real-game-a-beginners-guide/)
+- (December 8, 2017)
+  [Introducing ML-Agents Toolkit v0.2: Curriculum Learning, new environments, and more](https://blogs.unity3d.com/2017/12/08/introducing-ml-agents-v0-2-curriculum-learning-new-environments-and-more/)
+- (September 19, 2017)
+  [Introducing: Unity Machine Learning Agents Toolkit](https://blogs.unity3d.com/2017/09/19/introducing-unity-machine-learning-agents/)
+- Overviewing reinforcement learning concepts
-In addition to our own documentation, here are some additional, relevant articles:
+In addition to our own documentation, here are some additional, relevant
+articles:
-* [A Game Developer Learns Machine Learning](https://mikecann.co.uk/machine-learning/a-game-developer-learns-machine-learning-intent/)
-* [Explore Unity Technologies ML-Agents Exclusively on Intel Architecture](https://software.intel.com/en-us/articles/explore-unity-technologies-ml-agents-exclusively-on-intel-architecture)
-* [ML-Agents Penguins tutorial](https://learn.unity.com/project/ml-agents-penguins)
+- [A Game Developer Learns Machine Learning](https://mikecann.co.uk/machine-learning/a-game-developer-learns-machine-learning-intent/)
+- [Explore Unity Technologies ML-Agents Exclusively on Intel Architecture](https://software.intel.com/en-us/articles/explore-unity-technologies-ml-agents-exclusively-on-intel-architecture)
+- [ML-Agents Penguins tutorial](https://learn.unity.com/project/ml-agents-penguins)
 
 ## Community and Feedback
 
 
 For problems with the installation and setup of the the ML-Agents Toolkit, or
 discussions about how to best setup or train your agents, please create a new
-thread on the [Unity ML-Agents forum](https://forum.unity.com/forums/ml-agents.453/)
-and make sure to include as much detail as possible.
-If you run into any other problems using the ML-Agents Toolkit, or have a specific
-feature requests, please [submit a GitHub issue](https://github.com/Unity-Technologies/ml-agents/issues).
+thread on the
+[Unity ML-Agents forum](https://forum.unity.com/forums/ml-agents.453/) and make
+sure to include as much detail as possible. If you run into any other problems
+using the ML-Agents Toolkit, or have a specific feature requests, please
+[submit a GitHub issue](https://github.com/Unity-Technologies/ml-agents/issues).
-Your opinion matters a great deal to us. Only by hearing your thoughts on the Unity ML-Agents
-Toolkit can we continue to improve and grow. Please take a few minutes to
+Your opinion matters a great deal to us. Only by hearing your thoughts on the
+Unity ML-Agents Toolkit can we continue to improve and grow. Please take a few
+minutes to
-For any other questions or feedback, connect directly with the ML-Agents
-team at ml-agents@unity3d.com.
-
+For any other questions or feedback, connect directly with the ML-Agents team at
+ml-agents@unity3d.com.
 
 ## License
 

SURVEY.md

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

com.unity.ml-agents/CHANGELOG.md

 ### Major Changes
 #### com.unity.ml-agents (C#)
 #### ml-agents / ml-agents-envs / gym-unity (Python)
+- `max_step` in the `TerminalStep` and `TerminalSteps` objects was renamed `interrupted`.
+- Curriculum and Parameter Randomization configurations have been merged
+  into the main training configuration file. Note that this means training
+  configuration files are now environment-specific. (#3791)
+- Training artifacts (trained models, summaries) are now found in the `results/`
+  directory. (#3829)
+- Unity Player logs are now written out to the results directory. (#3877)
+- Run configuration YAML files are written out to the results directory at the end of the run. (#3815)
-## [1.0.0-preview] - 2020-05-06
+## [1.0.0-preview] - 2020-04-30
-- Added new 3-joint Worm ragdoll environment. (#3798)
-- The `--load` and `--train` command-line flags have been deprecated. Training
-  now happens by default, and use `--resume` to resume training instead. (#3705)
-- The Jupyter notebooks have been removed from the repository.
-- Removed the multi-agent gym option from the gym wrapper. For multi-agent
-  scenarios, use the [Low Level Python API](../docs/Python-API.md).
-- The low level Python API has changed. You can look at the document
-  [Low Level Python API documentation](../docs/Python-API.md) for more
-  information. If you use `mlagents-learn` for training, this should be a
-  transparent change.
-- Added ability to start training (initialize model weights) from a previous run
-  ID. (#3710)
-- The internal event `Academy.AgentSetStatus` was renamed to
-  `Academy.AgentPreStep` and made public.
-- The offset logic was removed from DecisionRequester.
-- The signature of `Agent.Heuristic()` was changed to take a `float[]` as a
+
+#### com.unity.ml-agents (C#)
+
+- The `MLAgents` C# namespace was renamed to `Unity.MLAgents`, and other nested
+  namespaces were similarly renamed. (#3843)
+- The offset logic was removed from DecisionRequester. (#3716)
+- The signature of `Agent.Heuristic()` was changed to take a float array as a
-  source of error where users would return arrays of the wrong size.
+  source of error where users would return arrays of the wrong size. (#3765)
-  communication between Unity and the Python process.
+  communication between Unity and the Python process. (#3760)
-  `AgentAction` and `AgentReset` have been removed.
-- The GhostTrainer has been extended to support asymmetric games and the
-  asymmetric example environment Strikers Vs. Goalie has been added.
-- The SideChannel API has changed (#3833, #3660) :
+  `AgentAction` and `AgentReset` have been removed. (#3770)
+- The SideChannel API has changed:
-  channels.
-  - `EnvironmentParameters` replaces the default `FloatProperties`.
-  You can access the `EnvironmentParameters` with
-  `Academy.Instance.EnvironmentParameters` on C# and create an
-  `EnvironmentParametersChannel` on Python
+    channels. (#3807)
+  - `Academy.FloatProperties` was replaced by `Academy.EnvironmentParameters`.
+    See the [Migration Guide](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Migrating.md)
+    for more details on upgrading. (#3807)
-  which is used when trying to read more data than the message contains.
+    which is used when trying to read more data than the message contains. (#3751)
-  (and other python StatsWriters). To do this from your code, use
-  `Academy.Instance.StatsRecorder.Add(key, value)`(#3660)
-- CameraSensorComponent.m_Grayscale and RenderTextureSensorComponent.m_Grayscale
-  were changed from `public` to `private` (#3808).
-- The `UnityEnv` class from the `gym-unity` package was renamed
-  `UnityToGymWrapper` and no longer creates the `UnityEnvironment`.
-  Instead, the `UnityEnvironment` must be passed as input to the
-  constructor of `UnityToGymWrapper`
+    (and other python StatsWriters). To do this from your code, use
+    `Academy.Instance.StatsRecorder.Add(key, value)`. (#3660)
+- `CameraSensorComponent.m_Grayscale` and
+  `RenderTextureSensorComponent.m_Grayscale` were changed from `public` to
+  `private`. These can still be accessed via their corresponding properties.
+  (#3808)
-- Update Barracuda to 0.7.0-preview which has breaking namespace and assembly name changes.
+- `WriteAdapter` was renamed to `ObservationWriter`. If you have a custom
+  `ISensor` implementation, you will need to change the signature of its
+  `Write()` method. (#3834)
+- The Barracuda dependency was upgraded to 0.7.0-preview (which has breaking
+  namespace and assembly name changes). (#3875)
+
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+
+- The `--load` and `--train` command-line flags have been deprecated. Training
+  now happens by default, and use `--resume` to resume training instead of
+  `--load`. (#3705)
+- The Jupyter notebooks have been removed from the repository. (#3704)
+- The multi-agent gym option was removed from the gym wrapper. For multi-agent
+  scenarios, use the [Low Level Python API](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Python-API.md). (#3681)
+- The low level Python API has changed. You can look at the document
+  [Low Level Python API](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Python-API.md)
+  documentation for more information. If you use `mlagents-learn` for training, this should be a
+  transparent change. (#3681)
+- Added ability to start training (initialize model weights) from a previous run
+  ID. (#3710)
+- The GhostTrainer has been extended to support asymmetric games and the
+  asymmetric example environment Strikers Vs. Goalie has been added. (#3653)
+- The `UnityEnv` class from the `gym-unity` package was renamed
+  `UnityToGymWrapper` and no longer creates the `UnityEnvironment`. Instead, the
+  `UnityEnvironment` must be passed as input to the constructor of
+  `UnityToGymWrapper` (#3812)
+#### com.unity.ml-agents (C#)
+
+- Added new 3-joint Worm ragdoll environment. (#3798)
+- `StackingSensor` was changed from `internal` visibility to `public`. (#3701)
+- The internal event `Academy.AgentSetStatus` was renamed to
+  `Academy.AgentPreStep` and made public. (#3716)
+- Academy.InferenceSeed property was added. This is used to initialize the
+  random number generator in ModelRunner, and is incremented for each
+  ModelRunner. (#3823)
+- `Agent.GetObservations()` was added, which returns a read-only view of the
+  observations added in `CollectObservations()`. (#3825)
+- `UnityRLCapabilities` was added to help inform users when RL features are
+  mismatched between C# and Python packages. (#3831)
+
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+
+  (#3646)
-  package version numbers.
+  package version numbers. (#3758)
-  environment port) will be used.
-- Fixed an issue where exceptions from environments provided a returncode of 0.
-  (#3680)
+  environment port) will be used. (#3673)
-- `StackingSensor` was changed from `internal` visibility to `public`
-- Academy.InferenceSeed property was added. This is used to initialize the
-  random number generator in ModelRunner, and is incremented for each ModelRunner. (#3823)
-- Added `Agent.GetObservations(), which returns a read-only view of the observations
-  added in `CollectObservations()`. (#3825)
-- Model updates can now happen asynchronously with environment steps for better performance. (#3690)
-- `num_updates` and `train_interval` for SAC were replaced with `steps_per_update`. (#3690)
-- `WriteAdapter` was renamed to `ObservationWriter`. If you have a custom `ISensor` implementation,
-you will need to change the signature of its `Write()` method. (#3834)
-- The maximum compatible version of tensorflow was changed to allow tensorflow 2.1 and 2.2. This
-will allow use with python 3.8 using tensorflow 2.2.0rc3.
-- `UnityRLCapabilities` was added to help inform users when RL features are mismatched between C# and Python packages. (#3831)
+- Model updates can now happen asynchronously with environment steps for better
+  performance. (#3690)
+- `num_updates` and `train_interval` for SAC were replaced with
+  `steps_per_update`. (#3690)
+- The maximum compatible version of tensorflow was changed to allow tensorflow
+  2.1 and 2.2. This will allow use with python 3.8 using tensorflow 2.2.0rc3.
+  (#3830)
+- `mlagents-learn` will no longer set the width and height of the executable
+  window to 84x84 when no width nor height arguments are given. (#3867)
- - Fixed a display bug when viewing Demonstration files in the inspector. The
-   shapes of the observations in the file now display correctly. (#3771)
+#### com.unity.ml-agents (C#)
+
+- Fixed a display bug when viewing Demonstration files in the inspector. The
+  shapes of the observations in the file now display correctly. (#3771)
+
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+
+- Fixed an issue where exceptions from environments provided a return code of 0.
+  (#3680)
+- Self-Play team changes will now trigger a full environment reset. This
+  prevents trajectories in progress during a team change from getting into the
+  buffer. (#3870)
 
 ## [0.15.1-preview] - 2020-03-30
 

com.unity.ml-agents/CONTRIBUTING.md

 
 ## Communication
 
-First, please read through our [code of conduct](https://github.com/Unity-Technologies/ml-agents/blob/master/CODE_OF_CONDUCT.md), as we
-expect all our contributors to follow it.
+First, please read through our
+[code of conduct](https://github.com/Unity-Technologies/ml-agents/blob/master/CODE_OF_CONDUCT.md),
+as we expect all our contributors to follow it.
-[Issues page](https://github.com/Unity-Technologies/ml-agents/issues)
-and briefly outlining the changes you plan to make. This will enable us to
-provide some context that may be helpful for you. This could range from advice
-and feedback on how to optimally perform your changes or reasons for not doing
-it.
+[Issues page](https://github.com/Unity-Technologies/ml-agents/issues) and
+briefly outlining the changes you plan to make. This will enable us to provide
+some context that may be helpful for you. This could range from advice and
+feedback on how to optimally perform your changes or reasons for not doing it.
-Lastly, if you're looking for input on what to contribute, feel free to
-reach out to us directly at ml-agents@unity3d.com and/or browse the GitHub
-issues with the `contributions welcome` label.
+Lastly, if you're looking for input on what to contribute, feel free to reach
+out to us directly at ml-agents@unity3d.com and/or browse the GitHub issues with
+the `contributions welcome` label.
-The master branch corresponds to the most recent version of the project.
-Note that this may be newer that the [latest release](https://github.com/Unity-Technologies/ml-agents/releases/tag/latest_release).
-When contributing to the project, please make sure that your Pull Request (PR) contains the following:
+The master branch corresponds to the most recent version of the project. Note
+that this may be newer that the
+[latest release](https://github.com/Unity-Technologies/ml-agents/releases/tag/latest_release).
+
+When contributing to the project, please make sure that your Pull Request (PR)
+contains the following:
-* Detailed description of the changes performed
-* Corresponding changes to documentation, unit tests and sample environments (if
+- Detailed description of the changes performed
+- Corresponding changes to documentation, unit tests and sample environments (if
-* Summary of the tests performed to validate your changes
-* Issue numbers that the PR resolves (if any)
+- Summary of the tests performed to validate your changes
+- Issue numbers that the PR resolves (if any)
-examples, as long as they are small, simple, demonstrate a unique feature of
-the platform, and provide a unique non-trivial challenge to modern
-machine learning algorithms. Feel free to submit these environments with a
-PR explaining the nature of the environment and task.
+examples, as long as they are small, simple, demonstrate a unique feature of the
+platform, and provide a unique non-trivial challenge to modern machine learning
+algorithms. Feel free to submit these environments with a PR explaining the
+nature of the environment and task.
-Several static checks are run on the codebase using the [pre-commit framework](https://pre-commit.com/) during CI. To execute the same checks locally, install `pre-commit` and run `pre-commit run --all-files`. Some hooks (for example, `black`) will output the corrected version of the code; others (like `mypy`) may require more effort to fix.
+Several static checks are run on the codebase using the
+[pre-commit framework](https://pre-commit.com/) during CI. To execute the same
+checks locally, install `pre-commit` and run `pre-commit run --all-files`. Some
+hooks (for example, `black`) will output the corrected version of the code;
+others (like `mypy`) may require more effort to fix.
-All python code should be formatted with [`black`](https://github.com/ambv/black). Style and formatting for C# may be enforced later.
+
+All python code should be formatted with
+[`black`](https://github.com/ambv/black). Style and formatting for C# may be
+enforced later.
-We use [`mypy`](http://mypy-lang.org/) to perform static type checking on python code. Currently not all code is annotated but we will increase coverage over time. If you are adding or refactoring code, please
+
+We use [`mypy`](http://mypy-lang.org/) to perform static type checking on python
+code. Currently not all code is annotated but we will increase coverage over
+time. If you are adding or refactoring code, please
+
-2. Make sure that code calling or called by the modified code also has type annotations.
+2. Make sure that code calling or called by the modified code also has type
+   annotations.
-The [type hint cheat sheet](https://mypy.readthedocs.io/en/stable/cheat_sheet_py3.html) provides a good introduction to adding type hints.
+The
+[type hint cheat sheet](https://mypy.readthedocs.io/en/stable/cheat_sheet_py3.html)
+provides a good introduction to adding type hints.
-When you open a pull request, you will be asked to acknolwedge our Contributor License Agreement. We allow both individual contributions and contributions made on behalf of companies. We use an open source tool called CLA assistant. If you have any questions on our CLA, please [submit an issue](https://github.com/Unity-Technologies/ml-agents/issues) or email us at ml-agents@unity3d.com.
+When you open a pull request, you will be asked to acknolwedge our Contributor
+License Agreement. We allow both individual contributions and contributions made
+on behalf of companies. We use an open source tool called CLA assistant. If you
+have any questions on our CLA, please
+[submit an issue](https://github.com/Unity-Technologies/ml-agents/issues) or
+email us at ml-agents@unity3d.com.

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

 # About ML-Agents package (`com.unity.ml-agents`)
 
-The Unity ML-Agents package contains the C# SDK for the [Unity ML-Agents Toolkit].
+The Unity ML-Agents package contains the C# SDK for the [Unity ML-Agents
+Toolkit].
-The package allows you to convert any Unity scene to into a learning
-environment and train character behaviors using a variety of machine learning
-algorithms. Additionally, it allows you to embed these trained behaviors back into
-Unity scenes to control your characters. More specifically, the package provides
- the following core functionalities:
+The package allows you to convert any Unity scene to into a learning environment
+and train character behaviors using a variety of machine learning algorithms.
+Additionally, it allows you to embed these trained behaviors back into Unity
+scenes to control your characters. More specifically, the package provides the
+following core functionalities:
-* Define Agents: entities, or characters, whose behavior will be learned. Agents are entities
-  that generate observations (through sensors), take actions, and receive rewards from
-  the environment.
-* Define Behaviors: entities that specifiy how an agent should act. Multiple agents can
-  share the same Behavior and a scene may have multiple Behaviors.
-* Record demonstrations of an agent within the Editor. You can use demonstrations
-  to help train a behavior for that agent.
-* Embedding a trained behavior into the scene via the [Unity Inference Engine].
-  Embedded behaviors allow you to switch an Agent between learning and inference.
+- Define Agents: entities, or characters, whose behavior will be learned. Agents
+  are entities that generate observations (through sensors), take actions, and
+  receive rewards from the environment.
+- Define Behaviors: entities that specifiy how an agent should act. Multiple
+  agents can share the same Behavior and a scene may have multiple Behaviors.
+- Record demonstrations of an agent within the Editor. You can use
+  demonstrations to help train a behavior for that agent.
+- Embedding a trained behavior into the scene via the [Unity Inference Engine].
+  Embedded behaviors allow you to switch an Agent between learning and
+  inference.
-Note that the *ML-Agents* package does not contain the machine learning algorithms for training
-behaviors. The *ML-Agents* package only supports instrumenting a Unity scene, setting it up for
-training, and then embedding the trained model back into your Unity scene. The machine learning
-algorithms that orchestrate training are part of the companion [Python package].
-
+Note that the _ML-Agents_ package does not contain the machine learning
+algorithms for training behaviors. The _ML-Agents_ package only supports
+instrumenting a Unity scene, setting it up for training, and then embedding the
+trained model back into your Unity scene. The machine learning algorithms that
+orchestrate training are part of the companion [Python package].
-|**Location**|**Description**|
-|---|---|
-|*Documentation~*|Contains the documentation for the Unity package.|
-|*Editor*|Contains utilities for Editor windows and drawers.|
-|*Plugins*|Contains third-party DLLs.|
-|*Runtime*|Contains core C# APIs for integrating ML-Agents into your Unity scene. |
-|*Tests*|Contains the unit tests for the package.|
-
+| **Location**     | **Description**                                                        |
+| ---------------- | ---------------------------------------------------------------------- |
+| _Documentation~_ | Contains the documentation for the Unity package.                      |
+| _Editor_         | Contains utilities for Editor windows and drawers.                     |
+| _Plugins_        | Contains third-party DLLs.                                             |
+| _Runtime_        | Contains core C# APIs for integrating ML-Agents into your Unity scene. |
+| _Tests_          | Contains the unit tests for the package.                               |
+
-To install this *ML-Agents* package, follow the instructions in the [Package Manager documentation].
+To install this _ML-Agents_ package, follow the instructions in the [Package
+Manager documentation].
-
-This version of the Unity ML-Agents package is compatible with the following versions of the
-Unity Editor:
+This version of the Unity ML-Agents package is compatible with the following
+versions of the Unity Editor:
-* 2018.4 and later
+- 2018.4 and later
-
-## Known limitations
+## Known Limitations
-Training is limited to the Unity Editor and Standalone builds on Windows, MacOS, and Linux.  Your environment will default to inference mode if training is not supported or is not currently running.
+
+Training is limited to the Unity Editor and Standalone builds on Windows, MacOS,
+and Linux with the Mono scripting backend. Currently, training does not work
+with the IL2CPP scripting backend. Your environment will default to inference
+mode if training is not supported or is not currently running.
-Inference is executed via the [Unity Inference Engine](https://docs.unity3d.com/Packages/com.unity.barracuda@latest/index.html).
+
+Inference is executed via the
+[Unity Inference Engine](https://docs.unity3d.com/Packages/com.unity.barracuda@latest/index.html).
-- All platforms supported.
+
+All platforms supported.
-- All platforms supported except:
-  - WebGL and GLES 3/2 on Android / iPhone
- **NOTE:** Mobile platform support includes:
- - Vulkan for Android
- - Metal for iOS.
+All platforms supported except:
+
+- WebGL and GLES 3/2 on Android / iPhone
+
+**NOTE:** Mobile platform support includes:
+
+- Vulkan for Android
+- Metal for iOS.
 
 ### Headless Mode
 
 `Academy.Instance.EnvironmentStep()`
 
 ### Unity Inference Engine Models
+
-
-the documentation, you can checkout our
-[GitHUb Repository], which also includes a number of ways to [connect with us]
-including our [ML-Agents Forum].
+the documentation, you can checkout our [GitHUb Repository], which also includes
+a number of ways to [connect with us] including our [ML-Agents Forum].
-
-[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/latest_release/docs/Installation.md
-[GitHUb Repository]: https://github.com/Unity-Technologies/ml-agents
-[Python package]: https://github.com/Unity-Technologies/ml-agents
-[Execution Order of Event Functions]: https://docs.unity3d.com/Manual/ExecutionOrder.html
+[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_1_docs/docs/Installation.md
+[github repository]: https://github.com/Unity-Technologies/ml-agents
+[python package]: https://github.com/Unity-Technologies/ml-agents
+[execution order of event functions]: https://docs.unity3d.com/Manual/ExecutionOrder.html
-[ML-Agents Forum]: https://forum.unity.com/forums/ml-agents.453/
+[ml-agents forum]: https://forum.unity.com/forums/ml-agents.453/

com.unity.ml-agents/Documentation~/filter.yml

 apiRules:
 - exclude:
-    uidRegex: ^MLAgents\.Tests$
+    uidRegex: ^Unity.MLAgents\.Tests$
-    uidRegex: ^MLAgents\.CommunicatorObjects$
+    uidRegex: ^Unity.MLAgents\.CommunicatorObjects$
-    uidRegex: ^MLAgents\.Tests\.Communicator$
+    uidRegex: ^Unity.MLAgents\.Tests\.Communicator$
-    uidRegex: ^MLAgents\.Editor$
+    uidRegex: ^Unity.MLAgents\.Editor$
-    uidRegex: ^MLAgentsExamples$
+    uidRegex: ^Unity.MLAgentsExamples$
     type: Namespace
 

com.unity.ml-agents/Editor/AgentEditor.cs

 using UnityEngine;
 using UnityEditor;
 
-namespace MLAgents.Editor
+namespace Unity.MLAgents.Editor
 {
     /*
      This code is meant to modify the behavior of the inspector on Agent Components.

com.unity.ml-agents/Editor/BehaviorParametersEditor.cs

-using MLAgents.Sensors;
-using MLAgents.Policies;
+using Unity.MLAgents.Policies;
+using Unity.MLAgents.Sensors;
-namespace MLAgents.Editor
+namespace Unity.MLAgents.Editor
 {
     /*
      This code is meant to modify the behavior of the inspector on Agent Components.

com.unity.ml-agents/Editor/BrainParametersDrawer.cs

 using UnityEngine;
 using UnityEditor;
-using MLAgents.Policies;
+using Unity.MLAgents.Policies;
-namespace MLAgents.Editor
+namespace Unity.MLAgents.Editor
 {
     /// <summary>
     /// PropertyDrawer for BrainParameters. Defines how BrainParameters are displayed in the

com.unity.ml-agents/Editor/CameraSensorComponentEditor.cs

 using UnityEngine;
 using UnityEditor;
-using MLAgents.Sensors;
+using Unity.MLAgents.Sensors;
-namespace MLAgents.Editor
+namespace Unity.MLAgents.Editor
 {
     [CustomEditor(typeof(CameraSensorComponent))]
     [CanEditMultipleObjects]

com.unity.ml-agents/Editor/DemonstrationDrawer.cs

 using System.Collections.Generic;
 using System.Text;
 using UnityEditor;
-using MLAgents.Demonstrations;
-using MLAgents.Policies;
+using Unity.MLAgents.Demonstrations;
+using Unity.MLAgents.Policies;
-namespace MLAgents.Editor
+namespace Unity.MLAgents.Editor
 {
     /// <summary>
     /// Renders a custom UI for DemonstrationSummary ScriptableObject.

com.unity.ml-agents/Editor/DemonstrationImporter.cs

 using System;
 using System.Collections.Generic;
 using System.IO;
-using MLAgents.CommunicatorObjects;
+using Unity.MLAgents.CommunicatorObjects;
-using MLAgents.Demonstrations;
+using Unity.MLAgents.Demonstrations;
-namespace MLAgents.Editor
+namespace Unity.MLAgents.Editor
 {
     /// <summary>
     /// Asset Importer used to parse demonstration files.

com.unity.ml-agents/Editor/EditorUtilities.cs

 using UnityEngine;
 
-namespace MLAgents.Editor
+namespace Unity.MLAgents.Editor
 {
     /// <summary>
     /// A static helper class for the Editor components of the ML-Agents SDK.

com.unity.ml-agents/Editor/RayPerceptionSensorComponentBaseEditor.cs

 using UnityEngine;
 using UnityEditor;
-using MLAgents.Sensors;
+using Unity.MLAgents.Sensors;
-namespace MLAgents.Editor
+namespace Unity.MLAgents.Editor
 {
     internal class RayPerceptionSensorComponentBaseEditor : UnityEditor.Editor
     {

com.unity.ml-agents/Editor/RenderTextureSensorComponentEditor.cs

 using UnityEngine;
 using UnityEditor;
-using MLAgents.Sensors;
-namespace MLAgents.Editor
+using Unity.MLAgents.Sensors;
+namespace Unity.MLAgents.Editor
 {
     [CustomEditor(typeof(RenderTextureSensorComponent))]
     [CanEditMultipleObjects]

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

 #if UNITY_EDITOR
 using UnityEditor;
 #endif
-using MLAgents.Inference;
-using MLAgents.Policies;
-using MLAgents.SideChannels;
+using Unity.MLAgents.Inference;
+using Unity.MLAgents.Policies;
+using Unity.MLAgents.SideChannels;
 using Unity.Barracuda;
 
 /**
  * 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/blob/0.15.1/docs/
+ * https://github.com/Unity-Technologies/ml-agents/tree/release_1_docs/docs/
-namespace MLAgents
+namespace Unity.MLAgents
 {
     /// <summary>
     /// Helper class to step the Academy during FixedUpdate phase.
     ///
     /// At initialization, the Academy attempts to connect to the Python training process through
     /// the external communicator. If successful, the training process can train <see cref="Agent"/>
-    /// instances. When you set an agent's <see cref="BehaviorParameters.behaviorType"/> setting
+    /// instances. When you set an agent's <see cref="BehaviorParameters.BehaviorType"/> setting
-    [HelpURL("https://github.com/Unity-Technologies/ml-agents/blob/master/" +
+    [HelpURL("https://github.com/Unity-Technologies/ml-agents/tree/release_1_docs/" +
-        /// When connecting to python, this must match UnityEnvironment.API_VERSION.
-        /// Currently we require strict equality between the communication protocol
-        /// on each side, although we may allow some flexibility in the future.
-        /// This should be incremented whenever a change is made to the communication protocol.
+        /// When connecting to python, this must be compatible with UnityEnvironment.API_VERSION.
+        /// We follow semantic versioning on the communication version, so existing
+        /// functionality will work as long the major versions match.
+        /// This should be changed whenever a change is made to the communication protocol.
-        const string k_ApiVersion = "0.17.0";
+        const string k_ApiVersion = "1.0.0";
 
         /// <summary>
         /// Unity package version of com.unity.ml-agents.
 
         const int k_EditorTrainingPort = 5004;
 
-        const string k_portCommandLineFlag = "--mlagents-port";
+        const string k_PortCommandLineFlag = "--mlagents-port";
 
         // Lazy initializer pattern, see https://csharpindepth.com/articles/singleton#lazy
         static Lazy<Academy> s_Lazy = new Lazy<Academy>(() => new Academy());
             var inputPort = "";
             for (var i = 0; i < args.Length; i++)
             {
-                if (args[i] == k_portCommandLineFlag)
+                if (args[i] == k_PortCommandLineFlag)
                 {
                     inputPort = args[i + 1];
                 }
             }
         }
 
-        private EnvironmentParameters m_EnvironmentParameters;
-        private StatsRecorder m_StatsRecorder;
+        EnvironmentParameters m_EnvironmentParameters;
+        StatsRecorder m_StatsRecorder;
 
         /// <summary>
         /// Returns the <see cref="EnvironmentParameters"/> instance. If training

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

 using System.Collections.ObjectModel;
 using UnityEngine;
 using Unity.Barracuda;
-using MLAgents.Sensors;
-using MLAgents.Demonstrations;
-using MLAgents.Policies;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgents.Demonstrations;
+using Unity.MLAgents.Policies;
-namespace MLAgents
+namespace Unity.MLAgents
 {
     /// <summary>
     /// Struct that contains all the information for an Agent, including its
     /// Use the <see cref="OnActionReceived"/> function to implement the actions your agent can take,
     /// such as moving to reach a goal or interacting with its environment.
     ///
-    /// When you call <see cref="EndEpisode"/> on an agent or the agent reaches its <see cref="maxStep"/> count,
+    /// When you call <see cref="EndEpisode"/> on an agent or the agent reaches its <see cref="MaxStep"/> count,
     /// its current episode ends. You can reset the agent -- or remove it from the
     /// environment -- by implementing the <see cref="OnEpisodeBegin"/> function. An agent also
     /// becomes done when the <see cref="Academy"/> resets the environment, which only happens when
     /// [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/0.15.1/docs/Learning-Environment-Design-Agents.md
-    /// [Reinforcement Learning in Unity]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Learning-Environment-Design.md
+    /// [Agents]: https://github.com/Unity-Technologies/ml-agents/blob/release_1_docs/docs/Learning-Environment-Design-Agents.md
+    /// [Reinforcement Learning in Unity]: https://github.com/Unity-Technologies/ml-agents/blob/release_1_docs/docs/Learning-Environment-Design.md
-    /// [Unity ML-Agents Toolkit manual]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Readme.md
+    /// [Unity ML-Agents Toolkit manual]: https://github.com/Unity-Technologies/ml-agents/blob/release_1_docs/docs/Readme.md
-    [HelpURL("https://github.com/Unity-Technologies/ml-agents/blob/master/" +
+    [HelpURL("https://github.com/Unity-Technologies/ml-agents/blob/release_1_docs/" +
         "docs/Learning-Environment-Design-Agents.md")]
     [Serializable]
     [RequireComponent(typeof(BehaviorParameters))]
         /// outside of training, you can set the max step to 0 in <see cref="Initialize"/>
         /// if the <see cref="Academy"/> is not connected to an external process.
         /// <code>
-        /// using MLAgents;
+        /// using Unity.MLAgents;
         ///
         /// public class MyAgent : Agent
         /// {
         internal VectorSensor collectObservationsSensor;
 
         /// <summary>
-        /// Called when the attached <see cref="GameObject"/> becomes enabled and active.
+        /// Called when the attached [GameObject] becomes enabled and active.
+        /// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
         /// </summary>
         /// <remarks>
         /// This function initializes the Agent instance, if it hasn't been initialized yet.
         }
 
         /// <summary>
-        /// Called when the attached <see cref="GameObject"/> becomes disabled and inactive.
+        /// Called when the attached [GameObject] becomes disabled and inactive.
+        /// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
         /// </summary>
         /// <remarks>
         /// Always call the base Agent class version of this function if you implement `OnDisable()`
         /// 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/0.15.1/docs/Learning-Environment-Design-Agents.md#rewards
-        /// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Reward-Signals.md
+        /// [Agents - Rewards]: https://github.com/Unity-Technologies/ml-agents/blob/release_1_docs/docs/Learning-Environment-Design-Agents.md#rewards
+        /// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/release_1_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/0.15.1/docs/Learning-Environment-Design-Agents.md#rewards
-        /// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Reward-Signals.md
+        /// [Agents - Rewards]: https://github.com/Unity-Technologies/ml-agents/blob/release_1_docs/docs/Learning-Environment-Design-Agents.md#rewards
+        /// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/release_1_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)
         /// control of an agent using keyboard, mouse, or game controller input.
         ///
         /// Your heuristic implementation can use any decision making logic you specify. Assign decision
-        /// values to the float[] array, <paramref cref="actionsOut"/>, passed to your function as a parameter.
+        /// values to the float[] array, <paramref name="actionsOut"/>, passed to your function as a parameter.
         /// Add values to the array at the same indexes as they are used in your
         /// <seealso cref="OnActionReceived(float[])"/> function, which receives this array and
         /// implements the corresponding agent behavior. See [Actions] for more information
         /// 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/0.15.1/docs/Training-Imitation-Learning.md#recording-demonstrations
-        /// [Actions]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Learning-Environment-Design-Agents.md#actions
+        /// [Demonstration Recorder]: https://github.com/Unity-Technologies/ml-agents/blob/release_1_docs/docs/Learning-Environment-Design-Agents.md#recording-demonstrations
+        /// [Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_1_docs/docs/Learning-Environment-Design-Agents.md#actions
         /// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
         /// </remarks>
         /// <example>
 
         /// <summary>
         /// Set up the list of ISensors on the Agent. By default, this will select any
-        /// SensorBase's attached to the Agent.
+        /// SensorComponent's attached to the Agent.
         /// </summary>
         internal void InitializeSensors()
         {
         /// 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/0.15.1/docs/Learning-Environment-Design-Agents.md#observations-and-sensors
+        /// [Observations and Sensors]: https://github.com/Unity-Technologies/ml-agents/blob/release_1_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/master/docs/Learning-Environment-Design-Agents.md#actions
+        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_1_docs/docs/Learning-Environment-Design-Agents.md#actions
         /// </remarks>
         /// <seealso cref="OnActionReceived(float[])"/>
         public virtual void CollectDiscreteActionMasks(DiscreteActionMasker actionMasker)
         ///
         /// For more information about implementing agent actions see [Agents - Actions].
         ///
-        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Learning-Environment-Design-Agents.md#actions
+        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_1_docs/docs/Learning-Environment-Design-Agents.md#actions
         /// </remarks>
         /// <param name="vectorAction">
         /// An array containing the action vector. The length of the array is specified

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

 using System.Collections.Generic;
 using System.Linq;
 using Google.Protobuf;
-using MLAgents.CommunicatorObjects;
+using Unity.MLAgents.CommunicatorObjects;
-using MLAgents.Sensors;
-using MLAgents.Demonstrations;
-using MLAgents.Policies;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgents.Demonstrations;
+using Unity.MLAgents.Policies;
-namespace MLAgents
+namespace Unity.MLAgents
 {
     internal static class GrpcExtensions
     {

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

 using System;
 using System.Collections.Generic;
-using MLAgents.Policies;
-using MLAgents.Sensors;
-using MLAgents.SideChannels;
+using Unity.MLAgents.Policies;
+using Unity.MLAgents.Sensors;
-namespace MLAgents
+namespace Unity.MLAgents
 {
     internal struct CommunicatorInitParameters
     {

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

 using System.Collections.Generic;
 using System.Linq;
 using UnityEngine;
-using MLAgents.CommunicatorObjects;
-using MLAgents.Sensors;
-using MLAgents.Policies;
-using MLAgents.SideChannels;
+using Unity.MLAgents.CommunicatorObjects;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgents.Policies;
+using Unity.MLAgents.SideChannels;
-namespace MLAgents
+namespace Unity.MLAgents
 {
     /// Responsible for communication with External using gRPC.
     internal class RpcCommunicator : ICommunicator

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

 using UnityEngine;
 
-namespace MLAgents
+namespace Unity.MLAgents
 {
     internal class UnityRLCapabilities
     {

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

-namespace MLAgents
+namespace Unity.MLAgents
 {
     /// <summary>
     /// Grouping for use in AddComponentMenu (instead of nesting the menus).

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

 using UnityEngine;
 using UnityEngine.Serialization;
 
-namespace MLAgents
+namespace Unity.MLAgents
 {
     /// <summary>
     /// The DecisionRequester component automatically request decisions for an

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

 using System;
 using UnityEngine;
-using MLAgents.Policies;
-namespace MLAgents.Demonstrations
+namespace Unity.MLAgents.Demonstrations
 {
     /// <summary>
     /// Demonstration meta-data.

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

 using System.Text.RegularExpressions;
 using UnityEngine;
 using System.IO;
-using MLAgents.Policies;
+using Unity.MLAgents.Policies;
-namespace MLAgents.Demonstrations
+namespace Unity.MLAgents.Demonstrations
 {
     /// <summary>
     /// The Demonstration Recorder component facilitates the recording of demonstrations
     /// 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/0.15.1/docs/Training-Imitation-Learning.md#recording-demonstrations
+    /// [Imitation Learning - Recording Demonstrations]: https://github.com/Unity-Technologies/ml-agents/blob/release_1_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/Demonstrations/DemonstrationSummary.cs

 using System;
 using System.Collections.Generic;
 using UnityEngine;
-using MLAgents.Policies;
+using Unity.MLAgents.Policies;
-namespace MLAgents.Demonstrations
+namespace Unity.MLAgents.Demonstrations
 {
     /// <summary>
     /// Summary of a loaded Demonstration file. Only used for display in the Inspector.

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

 using System.IO;
 using Google.Protobuf;
 using System.Collections.Generic;
-using MLAgents.Sensors;
-using MLAgents.Policies;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgents.Policies;
-namespace MLAgents.Demonstrations
+namespace Unity.MLAgents.Demonstrations
 {
     /// <summary>
     /// Responsible for writing demonstration data to stream (typically a file stream).
     {
         /// <summary>
-        /// Number of bytes reserved for the <see cref="Demonstration"/> metadata at the start of the demo file.
+        /// Number of bytes reserved for the <see cref="DemonstrationMetaData"/> at the start of the demo file.
         /// </summary>
         internal const int MetaDataBytes = 32;
 

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

 using System;
 using System.Collections.Generic;
 using System.Linq;
-using MLAgents.Policies;
+using Unity.MLAgents.Policies;
-namespace MLAgents
+namespace Unity.MLAgents
 {
     /// <summary>
     /// The DiscreteActionMasker class represents a set of masked (disallowed) actions and
         /// When used, the agent will not be able to perform the actions passed as argument
         /// at the next decision for the specified action branch. The actionIndices correspond
         /// to the action options the agent will be unable to perform.
-        /// 
+        ///
-        /// 
-        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Learning-Environment-Design-Agents.md#actions
+        ///
+        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_1_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/EnvironmentParameters.cs

 using System;
 using System.Collections.Generic;
-using MLAgents.SideChannels;
+using Unity.MLAgents.SideChannels;
-namespace MLAgents
+namespace Unity.MLAgents
 {
     /// <summary>
     /// A container for the Environment Parameters that may be modified during training.

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

-namespace MLAgents
+namespace Unity.MLAgents
 {
     internal static class EpisodeIdCounter
     {