Merge remote-tracking branch 'origin/master' into develop-add-fire

.circleci/config.yml

   python373:
     docker:
       - image: circleci/python:3.7.3
+  python382:
+    docker:
+      - image: circleci/python:3.8.2
 
 jobs:
   build_python:
       - run:
           name: Install Dependencies
           command: |
+            # Need ruby for search-and-replace
+            sudo apt-get update
+            sudo apt-get install ruby-full
             python3 -m venv venv
             . venv/bin/activate
             pip install --upgrade pip
           executor: python373
           pyversion: 3.7.3
           # Test python 3.7 with the newest supported versions
+          pip_constraints: test_constraints_max_tf2_version.txt
+      - build_python:
+          name: python_3.8.2+tf2.2
+          executor: python382
+          pyversion: 3.8.2
+          # Test python 3.8 with the newest edge versions
           pip_constraints: test_constraints_max_tf2_version.txt
       - markdown_link_check
       - pre-commit

.pre-commit-config.yaml

             )$
         args: [--score=n]
 
+- repo: https://github.com/mattlqx/pre-commit-search-and-replace
+  rev: v1.0.3
+  hooks:
+  - id: search-and-replace
+    types: [markdown]
+    exclude: ".*localized.*"
+
-        # markdown-link-check doesn't support multiple files on the commandline, so this hacks around that.
-        # Note that you must install the package separately via npm. For example:
-        #  brew install npm; npm install -g markdown-link-check
-        entry: bash -c 'for i in "$@"; do markdown-link-check -c markdown-link-check.fast.json "$i"; done' --
-        language: system
+        entry: utils/run_markdown_link_check.py
+        language: script
         types: [markdown]
         # Don't check localized files since their target might not be localized.
         exclude: ".*localized.*"
         name: markdown-link-check-full
-        entry: bash -c 'for i in "$@"; do markdown-link-check -c markdown-link-check.full.json "$i"; done' --
-        language: system
+        entry: utils/run_markdown_link_check.py
+        language: script
+        # Don't check localized files since their target might not be localized.
+        # Only run manually, e.g. pre-commit run --hook-stage manual markdown-link-check-full
+        args: [--check-remote]
     -   id: validate-versions
         name: validate library versions
         language: script

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

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

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

   - version: 2020.1
     coverageOptions: --enable-code-coverage --code-coverage-options 'generateHtmlReport;assemblyFilters:+Unity.ML-Agents'
     minCoveragePct: 72
+  - version: 2020.2
+    coverageOptions: --enable-code-coverage --code-coverage-options 'generateHtmlReport;assemblyFilters:+Unity.ML-Agents'
+    minCoveragePct: 72
+trunk_editor:
+  - version: trunk
+    coverageOptions: --enable-code-coverage --code-coverage-options 'generateHtmlReport;assemblyFilters:+Unity.ML-Agents'
+    minCoveragePct: 72
 test_platforms:
   - name: win
     type: Unity::VM
     flavor: b1.medium
 ---
 
+all_package_tests:
+  name: Run All Combinations of Editors/Platforms Tests
+  dependencies:
+  {% for editor in test_editors %}
+    {% for platform in test_platforms %}
+  - .yamato/com.unity.ml-agents-test.yml#test_{{ platform.name }}_{{ editor.version }}
+    {% endfor %}
+  {% endfor %}
+
+  {% for editor in trunk_editor %}
+    {% for platform in test_platforms %}
+  - .yamato/com.unity.ml-agents-test.yml#test_{{ platform.name }}_{{ editor.version }}
+    {% endfor %}
+  {% endfor %}
+  triggers:
+    cancel_old_ci: true
+    recurring:
+      - branch: master
+        frequency: daily
+
 {% for editor in test_editors %}
   {% for platform in test_platforms %}
 test_{{ platform.name }}_{{ editor.version }}:
     image: {{ platform.image }}
     flavor: {{ platform.flavor}}
   commands:
-    - npm install upm-ci-utils@stable -g --registry https://api.bintray.com/npm/unity/unity-npm
+    - npm install upm-ci-utils@stable -g --registry https://artifactory.prd.cds.internal.unity3d.com/artifactory/api/npm/upm-npm
     - upm-ci package test -u {{ editor.version }} --package-path com.unity.ml-agents {{ editor.coverageOptions }}
     - python ml-agents/tests/yamato/check_coverage_percent.py upm-ci~/test-results/ {{ editor.minCoveragePct }}
   artifacts:
     - .yamato/com.unity.ml-agents-pack.yml#pack
   triggers:
     cancel_old_ci: true
+    {% if platform.name == "mac" %}
+    {% endif %}
+  {% endfor %}
+{% endfor %}
-  {% endfor %}
+{% for editor in trunk_editor %}
+  {% for platform in test_platforms %}
+test_{{ platform.name }}_trunk:
+  name : com.unity.ml-agents test {{ editor.version }} on {{ platform.name }}
+  agent:
+    type: {{ platform.type }}
+    image: {{ platform.image }}
+    flavor: {{ platform.flavor}}
+  commands:
+    - python -m pip install unity-downloader-cli --extra-index-url https://artifactory.eu-cph-1.unityops.net/api/pypi/common-python/simple
+    - unity-downloader-cli -u trunk -c editor --wait --fast
+    - npm install upm-ci-utils@stable -g --registry https://artifactory.prd.cds.internal.unity3d.com/artifactory/api/npm/upm-npm
+    - upm-ci package test -u {{ editor.version }} --package-path com.unity.ml-agents {{ editor.coverageOptions }}
+    - python ml-agents/tests/yamato/check_coverage_percent.py upm-ci~/test-results/ {{ editor.minCoveragePct }}
+  artifacts:
+    logs:
+      paths:
+        - "upm-ci~/test-results/**/*"
+  dependencies:
+    - .yamato/com.unity.ml-agents-pack.yml#pack
+  triggers:
+    cancel_old_ci: true
+    {% endfor %}
   {% endfor %}

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

     [Header("Specific to Ball3D")]
     public GameObject ball;
     Rigidbody m_BallRb;
-    FloatPropertiesChannel m_ResetParams;
+    EnvironmentParameters m_ResetParams;
-        m_ResetParams = SideChannelUtils.GetSideChannel<FloatPropertiesChannel>();
+        m_ResetParams = Academy.Instance.EnvironmentParameters;
         SetResetParameters();
     }
 
     public void SetBall()
     {
         //Set the attributes of the ball by fetching the information from the academy
-        m_BallRb.mass = m_ResetParams.GetPropertyWithDefault("mass", 1.0f);
-        var scale = m_ResetParams.GetPropertyWithDefault("scale", 1.0f);
+        m_BallRb.mass = m_ResetParams.GetWithDefault("mass", 1.0f);
+        var scale = m_ResetParams.GetWithDefault("scale", 1.0f);
         ball.transform.localScale = new Vector3(scale, scale, scale);
     }
 

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

     [Header("Specific to Ball3DHard")]
     public GameObject ball;
     Rigidbody m_BallRb;
-    FloatPropertiesChannel m_ResetParams;
+    EnvironmentParameters m_ResetParams;
-        m_ResetParams = SideChannelUtils.GetSideChannel<FloatPropertiesChannel>();
+        m_ResetParams = Academy.Instance.EnvironmentParameters;
         SetResetParameters();
     }
 
     public void SetBall()
     {
         //Set the attributes of the ball by fetching the information from the academy
-        m_BallRb.mass = m_ResetParams.GetPropertyWithDefault("mass", 1.0f);
-        var scale = m_ResetParams.GetPropertyWithDefault("scale", 1.0f);
+        m_BallRb.mass = m_ResetParams.GetWithDefault("mass", 1.0f);
+        var scale = m_ResetParams.GetWithDefault("scale", 1.0f);
         ball.transform.localScale = new Vector3(scale, scale, scale);
     }
 

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

     int m_NumberJumps = 20;
     int m_JumpLeft = 20;
 
-    FloatPropertiesChannel m_ResetParams;
+    EnvironmentParameters m_ResetParams;
 
     public override void Initialize()
     {
-        m_ResetParams = SideChannelUtils.GetSideChannel<FloatPropertiesChannel>();
+        m_ResetParams = Academy.Instance.EnvironmentParameters;
 
         SetResetParameters();
     }
 
     public void SetTargetScale()
     {
-        var targetScale = m_ResetParams.GetPropertyWithDefault("target_scale", 1.0f);
+        var targetScale = m_ResetParams.GetWithDefault("target_scale", 1.0f);
         target.transform.localScale = new Vector3(targetScale, targetScale, targetScale);
     }
 

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

     public bool contribute;
     public bool useVectorObs;
 
+    EnvironmentParameters m_ResetParams;
 
     public override void Initialize()
     {
-
+        m_ResetParams = Academy.Instance.EnvironmentParameters;
         SetResetParameters();
     }
 
 
     public void SetLaserLengths()
     {
-        m_LaserLength = SideChannelUtils.GetSideChannel<FloatPropertiesChannel>().GetPropertyWithDefault("laser_length", 1.0f);
+        m_LaserLength = m_ResetParams.GetWithDefault("laser_length", 1.0f);
-        float agentScale = SideChannelUtils.GetSideChannel<FloatPropertiesChannel>().GetPropertyWithDefault("agent_scale", 1.0f);
+        float agentScale = m_ResetParams.GetWithDefault("agent_scale", 1.0f);
         gameObject.transform.localScale = new Vector3(agentScale, agentScale, agentScale);
     }
 

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

-using System;
-using MLAgents.SideChannels;
 
 public class FoodCollectorSettings : MonoBehaviour
 {
     public int totalScore;
     public Text scoreText;
 
-    StatsSideChannel m_statsSideChannel;
+    StatsRecorder m_Recorder;
-        m_statsSideChannel = SideChannelUtils.GetSideChannel<StatsSideChannel>();
+        m_Recorder = Academy.Instance.StatsRecorder;
-    public void EnvironmentReset()
+    private void EnvironmentReset()
     {
         ClearObjects(GameObject.FindGameObjectsWithTag("food"));
         ClearObjects(GameObject.FindGameObjectsWithTag("badFood"));
         // need to send every Update() call.
         if ((Time.frameCount % 100)== 0)
         {
-            m_statsSideChannel?.AddStat("TotalScore", totalScore);
+            m_Recorder.Add("TotalScore", totalScore);
         }
     }
 }

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

     const int k_Left = 3;
     const int k_Right = 4;
 
+    EnvironmentParameters m_ResetParams;
+
+    public override void Initialize()
+    {
+        m_ResetParams = Academy.Instance.EnvironmentParameters;
+    }
+
     public override void CollectDiscreteActionMasks(DiscreteActionMasker actionMasker)
     {
         // Mask the necessary actions if selected by the user.
             var positionX = (int)transform.position.x;
             var positionZ = (int)transform.position.z;
-            var maxPosition = (int)SideChannelUtils.GetSideChannel<FloatPropertiesChannel>().GetPropertyWithDefault("gridSize", 5f) - 1;
+            var maxPosition = (int)m_ResetParams.GetWithDefault("gridSize", 5f) - 1;
 
             if (positionX == 0)
             {

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

 
     public GameObject trueAgent;
 
-    FloatPropertiesChannel m_ResetParameters;
-
     Camera m_AgentCam;
 
     public GameObject goalPref;
 
     Vector3 m_InitialPosition;
 
+    EnvironmentParameters m_ResetParams;
+
-        m_ResetParameters = SideChannelUtils.GetSideChannel<FloatPropertiesChannel>();
+        m_ResetParams = Academy.Instance.EnvironmentParameters;
 
         m_Objects = new[] { goalPref, pitPref };
 
         m_InitialPosition = transform.position;
     }
 
-    public void SetEnvironment()
+    private void SetEnvironment()
-        transform.position = m_InitialPosition * (m_ResetParameters.GetPropertyWithDefault("gridSize", 5f) + 1);
+        transform.position = m_InitialPosition * (m_ResetParams.GetWithDefault("gridSize", 5f) + 1);
-        for (var i = 0; i < (int)m_ResetParameters.GetPropertyWithDefault("numObstacles", 1); i++)
+        for (var i = 0; i < (int)m_ResetParams.GetWithDefault("numObstacles", 1); i++)
-        for (var i = 0; i < (int)m_ResetParameters.GetPropertyWithDefault("numGoals", 1f); i++)
+        for (var i = 0; i < (int)m_ResetParams.GetWithDefault("numGoals", 1f); i++)
-        var gridSize = (int)m_ResetParameters.GetPropertyWithDefault("gridSize", 5f);
+        var gridSize = (int)m_ResetParams.GetWithDefault("gridSize", 5f);
         m_Plane.transform.localScale = new Vector3(gridSize / 10.0f, 1f, gridSize / 10.0f);
         m_Plane.transform.localPosition = new Vector3((gridSize - 1) / 2f, -0.5f, (gridSize - 1) / 2f);
         m_Sn.transform.localScale = new Vector3(1, 1, gridSize + 2);
 
     public void AreaReset()
     {
-        var gridSize = (int)m_ResetParameters.GetPropertyWithDefault("gridSize", 5f);
+        var gridSize = (int)m_ResetParams.GetWithDefault("gridSize", 5f);
         foreach (var actor in actorObjs)
         {
             DestroyImmediate(actor);
         {
             numbers.Add(Random.Range(0, gridSize * gridSize));
         }
-        var numbersA = Enumerable.ToArray(numbers);
+        var numbersA = numbers.ToArray();
 
         for (var i = 0; i < players.Length; i++)
         {

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

 
     public void Awake()
     {
-        SideChannelUtils.GetSideChannel<FloatPropertiesChannel>().RegisterCallback("gridSize", f =>
+        Academy.Instance.EnvironmentParameters.RegisterCallback("gridSize", f =>
         {
             MainCamera.transform.position = new Vector3(-(f - 1) / 2f, f * 1.25f, -(f - 1) / 2f);
             MainCamera.orthographicSize = (f + 5f) / 2f;

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

     {
         if (useVectorObs)
         {
-            sensor.AddObservation(StepCount / (float)maxStep);
+            sensor.AddObservation(StepCount / (float)MaxStep);
         }
     }
 
 
     public override void OnActionReceived(float[] vectorAction)
     {
-        AddReward(-1f / maxStep);
+        AddReward(-1f / MaxStep);
         MoveAgent(vectorAction);
     }
 

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

     /// </summary>
     Renderer m_GroundRenderer;
 
+    private EnvironmentParameters m_ResetParams;
+
     void Awake()
     {
         m_PushBlockSettings = FindObjectOfType<PushBlockSettings>();
         m_GroundRenderer = ground.GetComponent<Renderer>();
         // Starting material
         m_GroundMaterial = m_GroundRenderer.material;
+
+        m_ResetParams = Academy.Instance.EnvironmentParameters;
 
         SetResetParameters();
     }
         MoveAgent(vectorAction);
 
         // Penalty given each step to encourage agent to finish task quickly.
-        AddReward(-1f / maxStep);
+        AddReward(-1f / MaxStep);
     }
 
     public override void Heuristic(float[] actionsOut)
 
     public void SetGroundMaterialFriction()
     {
-        var resetParams = SideChannelUtils.GetSideChannel<FloatPropertiesChannel>();
-
-        groundCollider.material.dynamicFriction = resetParams.GetPropertyWithDefault("dynamic_friction", 0);
-        groundCollider.material.staticFriction = resetParams.GetPropertyWithDefault("static_friction", 0);
+        groundCollider.material.dynamicFriction = m_ResetParams.GetWithDefault("dynamic_friction", 0);
+        groundCollider.material.staticFriction = m_ResetParams.GetWithDefault("static_friction", 0);
-        var resetParams = SideChannelUtils.GetSideChannel<FloatPropertiesChannel>();
-
-        var scale = resetParams.GetPropertyWithDefault("block_scale", 2);
+        var scale = m_ResetParams.GetWithDefault("block_scale", 2);
-        m_BlockRb.drag = resetParams.GetPropertyWithDefault("block_drag", 0.5f);
+        m_BlockRb.drag = m_ResetParams.GetWithDefault("block_drag", 0.5f);
-    public void SetResetParameters()
+    private void SetResetParameters()
     {
         SetGroundMaterialFriction();
         SetBlockProperties();

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

 
     public override void OnActionReceived(float[] vectorAction)
     {
-        AddReward(-1f / maxStep);
+        AddReward(-1f / MaxStep);
         MoveAgent(vectorAction);
     }
 

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

     // Frequency of the cosine deviation of the goal along the vertical dimension
     float m_DeviationFreq;
 
+    private EnvironmentParameters m_ResetParams;
+
     /// <summary>
     /// Collect the rigidbodies of the reacher in order to resue them for
     /// observations and actions.
         m_RbA = pendulumA.GetComponent<Rigidbody>();
         m_RbB = pendulumB.GetComponent<Rigidbody>();
+
+        m_ResetParams = Academy.Instance.EnvironmentParameters;
 
         SetResetParameters();
     }
 
     public void SetResetParameters()
     {
-        var fp = SideChannelUtils.GetSideChannel<FloatPropertiesChannel>();
-        m_GoalSize = fp.GetPropertyWithDefault("goal_size", 5);
-        m_GoalSpeed = Random.Range(-1f, 1f) * fp.GetPropertyWithDefault("goal_speed", 1);
-        m_Deviation = fp.GetPropertyWithDefault("deviation", 0);
-        m_DeviationFreq = fp.GetPropertyWithDefault("deviation_freq", 0);
+        m_GoalSize = m_ResetParams.GetWithDefault("goal_size", 5);
+        m_GoalSpeed = Random.Range(-1f, 1f) * m_ResetParams.GetWithDefault("goal_speed", 1);
+        m_Deviation = m_ResetParams.GetWithDefault("deviation", 0);
+        m_DeviationFreq = m_ResetParams.GetWithDefault("deviation_freq", 0);
     }
 }

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

 using System;
 using System.Collections.Generic;
 using UnityEngine;
-using Barracuda;
+using Unity.Barracuda;
 using System.IO;
 using MLAgents;
 using MLAgents.Policies;
             if (m_MaxEpisodes > 0)
             {
                 // For Agents without maxSteps, exit as soon as we've hit the target number of episodes.
-                // For Agents that specify maxStep, also make sure we've gone at least that many steps.
+                // For Agents that specify MaxStep, also make sure we've gone at least that many steps.
-                if (m_Agent.CompletedEpisodes >= m_MaxEpisodes && m_NumSteps > m_MaxEpisodes * m_Agent.maxStep)
+                if (m_Agent.CompletedEpisodes >= m_MaxEpisodes && m_NumSteps > m_MaxEpisodes * m_Agent.MaxStep)
                 {
                     Application.Quit(0);
                 }
 
             if (!m_BehaviorNameOverrides.ContainsKey(behaviorName))
             {
-                Debug.Log($"No override for behaviorName {behaviorName}");
+                Debug.Log($"No override for BehaviorName {behaviorName}");
                 return null;
             }
 
         {
             m_Agent.LazyInitialize();
             var bp = m_Agent.GetComponent<BehaviorParameters>();
-            var name = bp.behaviorName;
+            var name = bp.BehaviorName;
 
             var nnModel = GetModelForBehaviorName(name);
             Debug.Log($"Overriding behavior {name} for agent with model {nnModel?.name}");

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

 
 namespace MLAgentsExamples
 {
+    /// <summary>
+    /// A helper class for the ML-Agents example scenes to override various
+    /// global settings, and restore them afterwards.
+    /// This can modify some Physics and time-stepping properties, so you
+    /// shouldn't copy it into your project unless you know what you're doing.
+    /// </summary>
     public class ProjectSettingsOverrides : MonoBehaviour
     {
         // Original values
             Physics.defaultSolverVelocityIterations = solverVelocityIterations;
 
             // Make sure the Academy singleton is initialized first, since it will create the SideChannels.
-            var academy = Academy.Instance;
-            SideChannelUtils.GetSideChannel<FloatPropertiesChannel>().RegisterCallback("gravity", f => { Physics.gravity = new Vector3(0, -f, 0); });
+            Academy.Instance.EnvironmentParameters.RegisterCallback("gravity", f => { Physics.gravity = new Vector3(0, -f, 0); });
         }
 
         public void OnDestroy()

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

 
         /// <summary>
         /// Default implementation of Write interface. This creates a temporary array,
-        /// calls WriteObservation, and then writes the results to the WriteAdapter.
+        /// calls WriteObservation, and then writes the results to the ObservationWriter.
-        /// <param name="adapter"></param>
+        /// <param name="writer"></param>
-        public virtual int Write(WriteAdapter adapter)
+        public virtual int Write(ObservationWriter writer)
         {
             // TODO reuse buffer for similar agents, don't call GetObservationShape()
             var numFloats = this.ObservationSize();
-            adapter.AddRange(buffer);
+            writer.AddRange(buffer);
 
             return numFloats;
         }

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

     BehaviorParameters m_BehaviorParameters;
     Vector3 m_Transform;
 
+    private EnvironmentParameters m_ResetParams;
+
-        m_Existential = 1f / maxStep;
+        m_Existential = 1f / MaxStep;
         m_BehaviorParameters = gameObject.GetComponent<BehaviorParameters>();
         if (m_BehaviorParameters.TeamId == (int)Team.Blue)
         {
             m_LateralSpeed = 0.3f;
             m_ForwardSpeed = 1.3f;
         }
-        else 
+        else
         {
             m_LateralSpeed = 0.3f;
             m_ForwardSpeed = 1.0f;
         area.playerStates.Add(playerState);
         m_PlayerIndex = area.playerStates.IndexOf(playerState);
         playerState.playerIndex = m_PlayerIndex;
+
+        m_ResetParams = Academy.Instance.EnvironmentParameters;
     }
 
     public void MoveAgent(float[] act)
     {
 
         timePenalty = 0;
-        m_BallTouch = SideChannelUtils.GetSideChannel<FloatPropertiesChannel>().GetPropertyWithDefault("ball_touch", 0);
+        m_BallTouch = m_ResetParams.GetWithDefault("ball_touch", 0);
         if (team == Team.Purple)
         {
             transform.rotation = Quaternion.Euler(0f, -90f, 0f);

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

     [HideInInspector]
     public bool canResetBall;
 
+    private EnvironmentParameters m_ResetParams;
+
     void Awake()
     {
         canResetBall = true;
         m_BallController.area = this;
         ballStartingPos = ball.transform.position;
+
+        m_ResetParams = Academy.Instance.EnvironmentParameters;
     }
 
     IEnumerator ShowGoalUI()
         ballRb.velocity = Vector3.zero;
         ballRb.angularVelocity = Vector3.zero;
 
-        var ballScale = SideChannelUtils.GetSideChannel<FloatPropertiesChannel>().GetPropertyWithDefault("ball_scale", 0.015f);
+        var ballScale = m_ResetParams.GetWithDefault("ball_scale", 0.015f);
         ballRb.transform.localScale = new Vector3(ballScale, ballScale, ballScale);
     }
 }

Project/Assets/ML-Agents/Examples/Tennis/Prefabs/TennisArea.prefab

   m_Name: 
   m_EditorClassIdentifier: 
   m_BrainParameters:
-    vectorObservationSize: 9
-    numStackedVectorObservations: 3
-    vectorActionSize: 03000000
-    vectorActionDescriptions: []
-    vectorActionSpaceType: 1
+    VectorObservationSize: 9
+    NumStackedVectorObservations: 3
+    VectorActionSize: 03000000
+    VectorActionDescriptions: []
+    VectorActionSpaceType: 1
   m_Model: {fileID: 11400000, guid: d6c5e749e4ceb4cf79640a5955706d3d, type: 3}
   m_InferenceDevice: 0
   m_BehaviorType: 0
   agentParameters:
     maxStep: 0
   hasUpgradedFromAgentParameters: 1
-  maxStep: 0
+  MaxStep: 0
   ball: {fileID: 1273406647218856}
   invertX: 0
   score: 0
   m_EditorClassIdentifier: 
   DecisionPeriod: 5
   TakeActionsBetweenDecisions: 1
-  offsetStep: 0
 --- !u!114 &6467897465973556822
 MonoBehaviour:
   m_ObjectHideFlags: 0
   m_Name: 
   m_EditorClassIdentifier: 
   m_BrainParameters:
-    vectorObservationSize: 9
-    numStackedVectorObservations: 3
-    vectorActionSize: 03000000
-    vectorActionDescriptions: []
-    vectorActionSpaceType: 1
+    VectorObservationSize: 9
+    NumStackedVectorObservations: 3
+    VectorActionSize: 03000000
+    VectorActionDescriptions: []
+    VectorActionSpaceType: 1
   m_Model: {fileID: 11400000, guid: d6c5e749e4ceb4cf79640a5955706d3d, type: 3}
   m_InferenceDevice: 0
   m_BehaviorType: 0
   agentParameters:
     maxStep: 0
   hasUpgradedFromAgentParameters: 1
-  maxStep: 0
+  MaxStep: 0
   ball: {fileID: 1273406647218856}
   invertX: 1
   score: 0
   m_EditorClassIdentifier: 
   DecisionPeriod: 5
   TakeActionsBetweenDecisions: 1
-  offsetStep: 0
 --- !u!114 &1420140102966759323
 MonoBehaviour:
   m_ObjectHideFlags: 0

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

     Rigidbody m_AgentRb;
     Rigidbody m_BallRb;
     float m_InvertMult;
-    FloatPropertiesChannel m_ResetParams;
+    EnvironmentParameters m_ResetParams;
 
     // Looks for the scoreboard based on the name of the gameObjects.
     // Do not modify the names of the Score GameObjects
         m_BallRb = ball.GetComponent<Rigidbody>();
         var canvas = GameObject.Find(k_CanvasName);
         GameObject scoreBoard;
-        m_ResetParams = SideChannelUtils.GetSideChannel<FloatPropertiesChannel>();
+        m_ResetParams = Academy.Instance.EnvironmentParameters;
         if (invertX)
         {
             scoreBoard = canvas.transform.Find(k_ScoreBoardBName).gameObject;
 
     public void SetRacket()
     {
-        angle = m_ResetParams.GetPropertyWithDefault("angle", 55);
+        angle = m_ResetParams.GetWithDefault("angle", 55);
         gameObject.transform.eulerAngles = new Vector3(
             gameObject.transform.eulerAngles.x,
             gameObject.transform.eulerAngles.y,
 
     public void SetBall()
     {
-        scale = m_ResetParams.GetPropertyWithDefault("scale", .5f);
+        scale = m_ResetParams.GetWithDefault("scale", .5f);
         ball.transform.localScale = new Vector3(scale, scale, scale);
     }
 

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

     Rigidbody m_ChestRb;
     Rigidbody m_SpineRb;
 
-    FloatPropertiesChannel m_ResetParams;
+    EnvironmentParameters m_ResetParams;
 
     public override void Initialize()
     {
         m_ChestRb = chest.GetComponent<Rigidbody>();
         m_SpineRb = spine.GetComponent<Rigidbody>();
 
-        m_ResetParams = SideChannelUtils.GetSideChannel<FloatPropertiesChannel>();
+        m_ResetParams = Academy.Instance.EnvironmentParameters;
 
         SetResetParameters();
     }
 
     public void SetTorsoMass()
     {
-        m_ChestRb.mass = m_ResetParams.GetPropertyWithDefault("chest_mass", 8);
-        m_SpineRb.mass = m_ResetParams.GetPropertyWithDefault("spine_mass", 10);
-        m_HipsRb.mass = m_ResetParams.GetPropertyWithDefault("hip_mass", 15);
+        m_ChestRb.mass = m_ResetParams.GetWithDefault("chest_mass", 8);
+        m_SpineRb.mass = m_ResetParams.GetWithDefault("spine_mass", 10);
+        m_HipsRb.mass = m_ResetParams.GetWithDefault("hip_mass", 15);
     }
 
     public void SetResetParameters()

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

 using System.Collections;
 using UnityEngine;
 using MLAgents;
-using Barracuda;
+using Unity.Barracuda;
 using MLAgents.Sensors;
 using MLAgents.SideChannels;
 
     Vector3 m_JumpTargetPos;
     Vector3 m_JumpStartingPos;
 
-    FloatPropertiesChannel m_FloatProperties;
+    EnvironmentParameters m_ResetParams;
 
     public override void Initialize()
     {
 
         spawnArea.SetActive(false);
 
-        m_FloatProperties = SideChannelUtils.GetSideChannel<FloatPropertiesChannel>();
+        m_ResetParams = Academy.Instance.EnvironmentParameters;
     }
 
     // Begin the jump sequence
         {
             localScale = new Vector3(
                 localScale.x,
-                m_FloatProperties.GetPropertyWithDefault("no_wall_height", 0),
+                m_ResetParams.GetWithDefault("no_wall_height", 0),
                 localScale.z);
             wall.transform.localScale = localScale;
             SetModel("SmallWallJump", noWallBrain);
             localScale = new Vector3(
                 localScale.x,
-                m_FloatProperties.GetPropertyWithDefault("small_wall_height", 4),
+                m_ResetParams.GetWithDefault("small_wall_height", 4),
                 localScale.z);
             wall.transform.localScale = localScale;
             SetModel("SmallWallJump", smallWallBrain);
-            var min = m_FloatProperties.GetPropertyWithDefault("big_wall_min_height", 8);
-            var max = m_FloatProperties.GetPropertyWithDefault("big_wall_max_height", 8);
+            var min = m_ResetParams.GetWithDefault("big_wall_min_height", 8);
+            var max = m_ResetParams.GetWithDefault("big_wall_max_height", 8);
             var height = min + Random.value * (max - min);
             localScale = new Vector3(
                 localScale.x,

Project/ProjectSettings/DynamicsManager.asset

 --- !u!55 &1
 PhysicsManager:
   m_ObjectHideFlags: 0
-  serializedVersion: 7
+  serializedVersion: 10
   m_Gravity: {x: 0, y: -9.81, z: 0}
   m_DefaultMaterial: {fileID: 0}
   m_BounceThreshold: 2
   m_LayerCollisionMatrix: ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffebffffffddffffffeffffffff5fffffffbffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff
   m_AutoSimulation: 1
   m_AutoSyncTransforms: 1
+  m_ReuseCollisionCallbacks: 0
   m_ClothInterCollisionSettingsToggle: 0
   m_ContactPairsMode: 0
   m_BroadphaseType: 0
   m_WorldSubdivisions: 8
+  m_FrictionType: 0
+  m_EnableEnhancedDeterminism: 0
+  m_EnableUnifiedHeightmaps: 1

com.unity.ml-agents/.npmignore

 build.sh.meta
 build.bat
 build.bat.meta
+upm-ci.log

com.unity.ml-agents/CHANGELOG.md

 [Semantic Versioning](http://semver.org/spec/v2.0.0.html).
 
 ## [Unreleased]
-
+#### com.unity.ml-agents (C#)
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+### Minor Changes
+#### com.unity.ml-agents (C#)
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+### Bug Fixes
+#### com.unity.ml-agents (C#)
+#### ml-agents / ml-agents-envs / gym-unity (Python)
+## [1.0.0-preview] - 2020-05-06
+
+### Major Changes
+- Added new 3-joint Worm ragdoll environment. (#3798)
-- Introduced the `SideChannelUtils` to register, unregister and access side
-  channels.
-- `Academy.FloatProperties` was removed, please use
-  `SideChannelUtils.GetSideChannel<FloatPropertiesChannel>()` instead.
 - 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
   communication between Unity and the Python process.
 - The obsolete `Agent` methods `GiveModel`, `Done`, `InitializeAgent`,
   `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 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) :
+  - Introduced the `SideChannelManager` to register, unregister and access side
+  channels.
+  - `EnvironmentParameters` replaces the default `FloatProperties`.
+  You can access the `EnvironmentParameters` with
+  `Academy.Instance.EnvironmentParameters` on C# and create an
+  `EnvironmentParametersChannel` on Python
+  - `SideChannel.OnMessageReceived` is now a protected method (was public)
+  - SideChannel IncomingMessages methods now take an optional default argument,
+  which is used when trying to read more data than the message contains.
+  - Added a feature to allow sending stats from C# environments to TensorBoard
+  (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`
+- Public fields and properties on several classes were renamed to follow Unity's
+  C# style conventions. All public fields and properties now use "PascalCase"
+  instead of "camelCase"; for example, `Agent.maxStep` was renamed to
+  `Agent.MaxStep`. For a full list of changes, see the pull request. (#3828)
+- Update Barracuda to 0.7.0-preview which has breaking namespace and assembly name changes.
-- Added a feature to allow sending stats from C# environments to TensorBoard
-  (and other python StatsWriters). To do this from your code, use
-  `SideChannelUtils.GetSideChannel<StatsSideChannel>().AddStat(key, value)`
-  (#3660)
-- SideChannel IncomingMessages methods now take an optional default argument,
-  which is used when trying to read more data than the message contains.
 - The way that UnityEnvironment decides the port was changed. If no port is
   specified, the behavior will depend on the `file_name` parameter. If it is
   `None`, 5004 (the editor port) will be used; otherwise 5005 (the base
 - Running `mlagents-learn` with the same `--run-id` twice will no longer
   overwrite the existing files. (#3705)
 - `StackingSensor` was changed from `internal` visibility to `public`
-- Updated Barracuda to 0.6.3-preview.
+- 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)
 
 ### Bug Fixes
 

com.unity.ml-agents/CONTRIBUTING.md

 # Contribution Guidelines
 
-Thank you for your interest in contributing to the ML-Agents toolkit! We are
+Thank you for your interest in contributing to the ML-Agents Toolkit! We are
-ML-Agents toolkit. To facilitate your contributions, we've outlined a brief set
+ML-Agents Toolkit. To facilitate your contributions, we've outlined a brief set
 of guidelines to ensure that your extensions can be easily integrated.
 
 ## Communication
 
 Second, before starting on a project that you intend to contribute to the
-ML-Agents toolkit (whether environments or modifications to the codebase), we
+ML-Agents Toolkit (whether environments or modifications to the codebase), we
 **strongly** recommend posting on our
 [Issues page](https://github.com/Unity-Technologies/ml-agents/issues)
 and briefly outlining the changes you plan to make. This will enable us to

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](https://github.com/Unity-Technologies/ml-agents).
+The Unity ML-Agents package contains the C# SDK for the [Unity ML-Agents Toolkit].
-The package provides the ability for any Unity scene to be converted into a learning
-environment where character behaviors can be trained using a variety of machine learning
-algorithms. Additionally, it enables any trained behavior to be embedded back into the Unity
-scene. More specifically, the package provides the following core functionalities:
-* Define Agents: entities 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. These demonstrations can be
-valuable to train a behavior for that agent.
-* Embedding a trained behavior into the scene via the
-[Unity Inference Engine](https://docs.unity3d.com/Packages/com.unity.barracuda@latest/index.html).
-Thus an Agent can switch from a learning behavior to an inference behavior.
+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:
-Note that this package does not contain the machine learning algorithms for training
-behaviors. It relies on a Python package to orchestrate the training. This package
-only enables instrumenting a Unity scene and setting it up for training, and then
-embedding the trained model back into your Unity scene.
+* 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.
-## Preview package
-This package is available as a preview, so it is not ready for production use.
-The features and documentation in this package might change before it is verified for release.
+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].
 
 
 ## Package contents
 |*Runtime*|Contains core C# APIs for integrating ML-Agents into your Unity scene. |
 |*Tests*|Contains the unit tests for the package.|
 
-<a name="Installation"></a>
+<a name="Installation"></a>
-To install this package, follow the instructions in the
-[Package Manager documentation](https://docs.unity3d.com/Manual/upm-ui-install.html).
+To install this *ML-Agents* package, follow the instructions in the [Package Manager documentation].
+
+To install the companion Python package to enable training behaviors, follow the
+[installation instructions] on our [GitHub repository].
-To install the Python package to enable training behaviors, follow the instructions on our
-[GitHub repository](https://github.com/Unity-Technologies/ml-agents/blob/latest_release/docs/Installation.md).
-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 (recommended)
+* 2018.4 and later
+
+### Training
+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.
+
+### Inference
+Inference is executed via the [Unity Inference Engine](https://docs.unity3d.com/Packages/com.unity.barracuda@latest/index.html).
+
+**CPU**
+- All platforms supported.
+
+**GPU**
+- 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
 
 If you enable Headless mode, you will not be able to collect visual observations
 Currently the speed of the game physics can only be increased to 100x real-time.
 The Academy also moves in time with FixedUpdate() rather than Update(), so game
 behavior implemented in Update() may be out of sync with the agent decision
-making. See
-[Execution Order of Event Functions](https://docs.unity3d.com/Manual/ExecutionOrder.html)
-for more information.
+making. See [Execution Order of Event Functions] for more information.
 
 You can control the frequency of Academy stepping by calling
 `Academy.Instance.DisableAutomaticStepping()`, and then calling
 
 If you are new to the Unity ML-Agents package, or have a question after reading
 the documentation, you can checkout our
-[GitHUb Repository](https://github.com/Unity-Technologies/ml-agents), which
-also includes a number of ways to
-[connect with us](https://github.com/Unity-Technologies/ml-agents#community-and-feedback)
-including our [ML-Agents Forum](https://forum.unity.com/forums/ml-agents.453/).
+[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
+[connect with us]: https://github.com/Unity-Technologies/ml-agents#community-and-feedback
+[ML-Agents Forum]: https://forum.unity.com/forums/ml-agents.453/

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

             var serializedAgent = serializedObject;
             serializedAgent.Update();
 
-            var maxSteps = serializedAgent.FindProperty(
-                "maxStep");
+            var maxSteps = serializedAgent.FindProperty("MaxStep");
-                new GUIContent(
-                    "Max Step", "The per-agent maximum number of steps."));
+                new GUIContent("Max Step", "The per-agent maximum number of steps.")
+            );
 
             serializedAgent.ApplyModifiedProperties();
 

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

 using MLAgents.Sensors;
 using UnityEditor;
-using Barracuda;
+using Unity.Barracuda;
 using MLAgents.Policies;
 using UnityEngine;
 
             var model = (NNModel)serializedObject.FindProperty("m_Model").objectReferenceValue;
             var behaviorParameters = (BehaviorParameters)target;
             SensorComponent[] sensorComponents;
-            if (behaviorParameters.useChildSensors)
+            if (behaviorParameters.UseChildSensors)
             {
                 sensorComponents = behaviorParameters.GetComponentsInChildren<SensorComponent>();
             }
             }
-            var brainParameters = behaviorParameters.brainParameters;
+            var brainParameters = behaviorParameters.BrainParameters;
             if (model != null)
             {
                 barracudaModel = ModelLoader.Load(model);
                 var failedChecks = Inference.BarracudaModelParamLoader.CheckModel(
-                    barracudaModel, brainParameters, sensorComponents, behaviorParameters.behaviorType
+                    barracudaModel, brainParameters, sensorComponents, behaviorParameters.BehaviorType
                 );
                 foreach (var check in failedChecks)
                 {

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

         // The height of a line in the Unity Inspectors
         const float k_LineHeight = 17f;
         const int k_VecObsNumLine = 3;
-        const string k_ActionSizePropName = "vectorActionSize";
-        const string k_ActionTypePropName = "vectorActionSpaceType";
-        const string k_ActionDescriptionPropName = "vectorActionDescriptions";
-        const string k_VecObsPropName = "vectorObservationSize";
-        const string k_NumVecObsPropName = "numStackedVectorObservations";
+        const string k_ActionSizePropName = "VectorActionSize";
+        const string k_ActionTypePropName = "VectorActionSpaceType";
+        const string k_ActionDescriptionPropName = "VectorActionDescriptions";
+        const string k_VecObsPropName = "VectorObservationSize";
+        const string k_NumVecObsPropName = "NumStackedVectorObservations";
 
         /// <inheritdoc />
         public override float GetPropertyHeight(SerializedProperty property, GUIContent label)

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

         /// </summary>
         void MakeActionsProperty(SerializedProperty property)
         {
-            var actSizeProperty = property.FindPropertyRelative("vectorActionSize");
-            var actSpaceTypeProp = property.FindPropertyRelative("vectorActionSpaceType");
+            var actSizeProperty = property.FindPropertyRelative("VectorActionSize");
+            var actSpaceTypeProp = property.FindPropertyRelative("VectorActionSpaceType");
 
             var vecActSizeLabel =
                 actSizeProperty.displayName + ": " + BuildIntArrayLabel(actSizeProperty);

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

 
 namespace MLAgents.Editor
 {
+    /// <summary>
+    /// A static helper class for the Editor components of the ML-Agents SDK.
+    /// </summary>
     public static class EditorUtilities
     {
         /// <summary>

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

             // it is not editable during play mode.
             EditorGUI.BeginDisabledGroup(!EditorUtilities.CanUpdateModelProperties());
             {
-                EditorGUILayout.PropertyField(so.FindProperty("m_ObservationStacks"), true);
+                EditorGUILayout.PropertyField(so.FindProperty("m_ObservationStacks"), new GUIContent("Stacked Raycasts"), true);
             }
             EditorGUI.EndDisabledGroup();
 

com.unity.ml-agents/Editor/Unity.ML-Agents.Editor.asmdef

     "name": "Unity.ML-Agents.Editor",
     "references": [
         "Unity.ML-Agents",
-        "Barracuda",
+        "Unity.Barracuda",
         "Unity.ML-Agents.CommunicatorObjects"
     ],
     "optionalUnityReferences": [],
     "precompiledReferences": [],
     "autoReferenced": true,
     "defineConstraints": []
-}
+}

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

 using MLAgents.Inference;
 using MLAgents.Policies;
 using MLAgents.SideChannels;
-using Barracuda;
+using Unity.Barracuda;
 
 /**
  * Welcome to Unity Machine Learning Agents (ML-Agents).
  * 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/master/docs/
+ * https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/
  */
 
 namespace MLAgents
     }
 
     /// <summary>
-    /// An Academy is where Agent objects go to train their behaviors.
+    /// The Academy singleton manages agent training and decision making.
-    /// When an academy is run, it can either be in inference or training mode.
-    /// The mode is determined by the presence or absence of a Communicator. In
-    /// the presence of a communicator, the academy is run in training mode where
-    /// the states and observations of each agent are sent through the
-    /// communicator. In the absence of a communicator, the academy is run in
-    /// inference mode where the agent behavior is determined by the Policy
-    /// attached to it.
+    /// Access the Academy singleton through the <see cref="Instance"/>
+    /// property. The Academy instance is initialized the first time it is accessed (which will
+    /// typically be by the first <see cref="Agent"/> initialized in a scene).
+    ///
+    /// 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
+    /// to <see cref="BehaviorType.Default"/>, the agent exchanges data with the training process
+    /// to make decisions. If no training process is available, agents with the default behavior
+    /// 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/blob/master/" +
         "docs/Learning-Environment-Design.md")]
         /// 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.
         /// </summary>
-        const string k_ApiVersion = "0.16.0";
+        const string k_ApiVersion = "0.17.0";
-        internal const string k_PackageVersion = "0.15.1-preview";
+        internal const string k_PackageVersion = "1.0.0-preview";
 
         const int k_EditorTrainingPort = 5004;
 
         static Lazy<Academy> s_Lazy = new Lazy<Academy>(() => new Academy());
 
         /// <summary>
-        /// True if the Academy is initialized, false otherwise.
+        ///Reports whether the Academy has been initialized yet.
+        /// <value><c>True</c> if the Academy is initialized, <c>false</c> otherwise.</value>
         public static bool IsInitialized