Merge branch 'master' into develop-hybrid-actions

.circleci/config.yml

       - image: circleci/python:3.8.2
 
 jobs:
-  build_python:
-    parameters:
-      executor:
-        type: executor
-      pyversion:
-        type: string
-        description: python version to being used (currently only affects caching).
-      pip_constraints:
-        type: string
-        description: Constraints file that is passed to "pip install". We constraint older versions of libraries for older python runtime, in order to help ensure compatibility.
-      enforce_onnx_conversion:
-        type: integer
-        default: 0
-        description: Whether to raise an exception if ONNX models couldn't be saved.
-    executor: << parameters.executor >>
-    working_directory: ~/repo
-
-    # Run additional numpy checks on unit tests
-    environment:
-      TEST_ENFORCE_NUMPY_FLOAT32: 1
-      TEST_ENFORCE_ONNX_CONVERSION: << parameters.enforce_onnx_conversion >>
-
-    steps:
-      - checkout
-
-      - run:
-          # Combine all the python dependencies into one file so that we can use that for the cache checksum
-          name: Combine pip dependencies for caching
-          command: cat ml-agents/setup.py ml-agents-envs/setup.py gym-unity/setup.py test_requirements.txt << parameters.pip_constraints >> > python_deps.txt
-
-      - restore_cache:
-          keys:
-          # Parameterize the cache so that different python versions can get different versions of the packages
-          - v1-dependencies-py<< parameters.pyversion >>-{{ checksum "python_deps.txt" }}
-
-      - run:
-          name: Install Dependencies
-          command: |
-            python3 -m venv venv
-            . venv/bin/activate
-            pip install --upgrade pip
-            pip install --upgrade setuptools
-            pip install --progress-bar=off -e ./ml-agents-envs -c << parameters.pip_constraints >>
-            pip install --progress-bar=off -e ./ml-agents -c << parameters.pip_constraints >>
-            pip install --progress-bar=off -r test_requirements.txt -c << parameters.pip_constraints >>
-            pip install --progress-bar=off -e ./gym-unity -c << parameters.pip_constraints >>
-
-      - save_cache:
-          paths:
-            - ./venv
-          key: v1-dependencies-py<< parameters.pyversion >>-{{ checksum "python_deps.txt" }}
-
-      - run:
-          name: Run Tests for ml-agents and gym_unity
-          # This also dumps the installed pip packages to a file, so we can see what versions are actually being used.
-          command: |
-            . venv/bin/activate
-            mkdir test-reports
-            pip freeze > test-reports/pip_versions.txt
-            pytest --cov=ml-agents --cov=ml-agents-envs --cov=gym-unity --cov-report html --junitxml=test-reports/junit.xml -p no:warnings
-
-      - run:
-          name: Verify there are no hidden/missing metafiles.
-          # Renaming files or deleting files can leave metafiles behind that makes Unity very unhappy.
-          command: |
-            . venv/bin/activate
-            python utils/validate_meta_files.py
-
-      - store_test_results:
-          path: test-reports
-
-      - store_artifacts:
-          path: test-reports
-          destination: test-reports
-
-      - store_artifacts:
-          path: htmlcov
-          destination: htmlcov
-
-  pre-commit:
-    docker:
-      - image: circleci/python:3.7.3
-    working_directory: ~/repo/
-
-    steps:
-      - checkout
-      - run:
-          name: Combine precommit config and python versions for caching
-          command: |
-            cat .pre-commit-config.yaml > pre-commit-deps.txt
-            python -VV >> pre-commit-deps.txt
-
-      - restore_cache:
-          keys:
-          - v1-precommit-deps-{{ checksum "pre-commit-deps.txt" }}
-
-      - run:
-          name: Install Dependencies
-          command: |
-            # Need ruby for search-and-replace
-            sudo apt-get update
-            sudo apt-get install ruby-full
-
-            # install dotnet and the formatter - see https://docs.microsoft.com/en-us/dotnet/core/install/linux-debian#debian-9-
-            pushd ~
-            wget -O - https://packages.microsoft.com/keys/microsoft.asc | gpg --dearmor > microsoft.asc.gpg
-            sudo mv microsoft.asc.gpg /etc/apt/trusted.gpg.d/
-            wget https://packages.microsoft.com/config/debian/9/prod.list
-            sudo mv prod.list /etc/apt/sources.list.d/microsoft-prod.list
-            sudo chown root:root /etc/apt/trusted.gpg.d/microsoft.asc.gpg
-            sudo chown root:root /etc/apt/sources.list.d/microsoft-prod.list
-            popd
-
-            sudo apt-get install -y apt-transport-https && \
-              sudo apt-get update && \
-              sudo apt-get install -y dotnet-sdk-3.1 && \
-              dotnet tool install -g dotnet-format --version 4.1.131201
-
-            echo "Setting up venv"
-            python3 -m venv venv
-            . venv/bin/activate
-            pip install --upgrade pip
-            pip install --upgrade setuptools
-            pip install pre-commit
-            # Install the hooks now so that they'll be cached
-            pre-commit install-hooks
-
-      - save_cache:
-          paths:
-            - ~/.cache/pre-commit
-            - ./venv
-          key: v1-precommit-deps-{{ checksum "pre-commit-deps.txt" }}
-
-      - run:
-          name: Check Code Style using pre-commit
-          command: |
-            . venv/bin/activate
-            export PATH="$PATH:~/.dotnet/tools"
-            pre-commit run --show-diff-on-failure --all-files
-
-  markdown_link_check:
-    parameters:
-      precommit_command:
-        type: string
-        description: precommit hook to run
-        default: markdown-link-check
-    docker:
-      - image: circleci/node:12.6.0
-    working_directory: ~/repo
-
-    steps:
-      - checkout
-
-      - restore_cache:
-          keys:
-          - v1-node-dependencies-{{ checksum ".pre-commit-config.yaml" }}
-          # fallback to using the latest cache if no exact match is found
-          - v1-node-dependencies-
-
-      - run:
-          name: Install Dependencies
-          command: |
-            sudo apt-get install python3-venv
-            python3 -m venv venv
-            . venv/bin/activate
-            pip install pre-commit
-      - run: sudo npm install -g markdown-link-check
-
-      - save_cache:
-          paths:
-            - ./venv
-          key: v1-node-dependencies-{{ checksum ".pre-commit-config.yaml" }}
-
-      - run:
-          name: Run markdown-link-check via precommit
-          command: |
-            . venv/bin/activate
-            pre-commit run --hook-stage manual << parameters.precommit_command >> --all-files
-
   deploy:
     parameters:
       directory:
   version: 2
   workflow:
     jobs:
-      - build_python:
-          name: python_3.6.1
-          executor: python361
-          pyversion: 3.6.1
-          # Test python 3.6 with the oldest supported versions
-          pip_constraints: test_constraints_min_version.txt
-      - build_python:
-          name: python_3.7.3
-          executor: python373
-          pyversion: 3.7.3
-          # Test python 3.7 with the newest supported versions
-          pip_constraints: test_constraints_max_tf1_version.txt
-          # Make sure ONNX conversion passes here (recent version of tensorflow 1.x)
-          enforce_onnx_conversion: 1
-      - build_python:
-          name: python_3.7.3+tf2
-          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
       # The first deploy jobs are the "real" ones that upload to pypi
       - deploy:
           name: deploy ml-agents-envs
               only: /^release_[0-9]+_test[0-9]+$/
             branches:
               ignore: /.*/
-  nightly:
-    triggers:
-      - schedule:
-          cron: "0 0 * * *"
-          filters:
-            branches:
-              only:
-                - develop
-    jobs:
-      - markdown_link_check:
-          name: markdown-link-check full
-          precommit_command: markdown-link-check-full

.pylintrc

 # Add files or directories to the ignore list. They should be base names, not
 # paths.
 ignore=CVS
+generated-members=torch.*
+
 
 [MESSAGES CONTROL]
 #enable=

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

 test_editors:
-  - version: 2019.3
+  - version: 2019.4
+  - version: 2020.2
 ---
 {% for editor in test_editors %}
 Run_Mac_Perfomance_Tests{{ editor.version }}:

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

   - version: 2018.4
     # 2018.4 doesn't support code-coverage
     enableCodeCoverage: !!bool false
-  - version: 2019.3
+  - version: 2019.4
     enableCodeCoverage: !!bool true
   - version: 2020.1
     enableCodeCoverage: !!bool true
     flavor: {{ platform.flavor}}
   commands:
     - npm install upm-ci-utils@stable -g --registry https://artifactory.prd.cds.internal.unity3d.com/artifactory/api/npm/upm-npm
-    - upm-ci project test -u {{ editor.version }} --project-path Project --package-filter {{ package.name }} {{ coverageOptions }}
+    - upm-ci project test -u {{ editor.version }} --project-path Project --package-filter {{ package.name }} {{ coverageOptions }} --extra-utr-arg "reruncount=2"
     {% if editor.enableCodeCoverage %}
     - python3 ml-agents/tests/yamato/check_coverage_percent.py upm-ci~/test-results/ {{ package.minCoveragePct }}
     {% endif %}
     - python -m pip install unity-downloader-cli --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple --upgrade
     - unity-downloader-cli -u trunk -c editor --wait --fast
     - npm install upm-ci-utils@stable -g --registry https://artifactory.prd.cds.internal.unity3d.com/artifactory/api/npm/upm-npm
-    - upm-ci project test -u {{ editor.version }} --project-path Project --package-filter {{ package.name }} {{ coverageOptions }}
+    - upm-ci project test -u {{ editor.version }} --project-path Project --package-filter {{ package.name }} {{ coverageOptions }} --extra-utr-arg "reruncount=2"
     {% if editor.enableCodeCoverage %}
     - python3 ml-agents/tests/yamato/check_coverage_percent.py upm-ci~/test-results/ {{ package.minCoveragePct }}
     {% endif %}

.yamato/gym-interface-test.yml

 test_editors:
-  - version: 2019.3
+  - version: 2019.4
 ---
 {% for editor in test_editors %}
 test_gym_interface_{{ editor.version }}:

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

 test_editors:
-  - version: 2019.3
+  - version: 2019.4
 ---
 {% for editor in test_editors %}
 test_mac_ll_api_{{ editor.version }}:

.yamato/training-int-tests.yml

 test_editors:
   - version: 2018.4
-  - version: 2019.3
+  - version: 2019.4
+  - version: 2020.1
 ---
 {% for editor in test_editors %}
 test_mac_training_int_{{ editor.version }}:

DevProject/.gitignore

 /Assets/Demonstrations*
 /Assets/ML-Agents/Timers*
 /csharp_timers.json
+/CodeCoverage/
 
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Project/Assets/ML-Agents/Examples/Crawler/Scripts/CrawlerAgent.cs

 using System;
 using UnityEngine;
 using Unity.MLAgents;
+using Unity.Barracuda;
 using Unity.MLAgents.Actuators;
 using Unity.MLAgentsExamples;
 using Unity.MLAgents.Sensors;
 public class CrawlerAgent : Agent
 {
-    public float maximumWalkingSpeed = 999; //The max walk velocity magnitude an agent will be rewarded for
-    Vector3 m_WalkDir; //Direction to the target
-    Quaternion m_WalkDirLookRot; //Will hold the rotation to our target
+    //The type of crawler behavior we want to use.
+    //This setting will determine how the agent is set up during initialization.
+    public enum CrawlerAgentBehaviorType
+    {
+        CrawlerDynamic,
+        CrawlerDynamicVariableSpeed,
+        CrawlerStatic,
+        CrawlerStaticVariableSpeed
+    }
+
+    [Tooltip(
+        "VariableSpeed - The agent will sample random speed magnitudes while training.\n" +
+        "Dynamic - The agent will run towards a target that changes position.\n" +
+        "Static - The agent will run towards a static target. "
+    )]
+    public CrawlerAgentBehaviorType typeOfCrawler;
+
+    //Crawler Brains
+    //A different brain will be used depending on the CrawlerAgentBehaviorType selected
+    [Header("NN Models")] public NNModel crawlerDyModel;
+    public NNModel crawlerDyVSModel;
+    public NNModel crawlerStModel;
+    public NNModel crawlerStVSModel;
+
+    [Header("Walk Speed")]
+    [Range(0.1f, m_maxWalkingSpeed)]
+    [SerializeField]
+    [Tooltip(
+        "The speed the agent will try to match.\n\n" +
+        "TRAINING:\n" +
+        "For VariableSpeed envs, this value will randomize at the start of each training episode.\n" +
+        "Otherwise the agent will try to match the speed set here.\n\n" +
+        "INFERENCE:\n" +
+        "During inference, VariableSpeed agents will modify their behavior based on this value " +
+        "whereas the CrawlerDynamic & CrawlerStatic agents will run at the speed specified during training "
+    )]
+    //The walking speed to try and achieve
+    private float m_TargetWalkingSpeed = m_maxWalkingSpeed;
-    [Header("Target To Walk Towards")]
-    [Space(10)]
-    public TargetController target; //Target the agent will walk towards.
+    const float m_maxWalkingSpeed = 15; //The max walking speed
+
+    //The current target walking speed. Clamped because a value of zero will cause NaNs
+    public float TargetWalkingSpeed
+    {
+        get { return m_TargetWalkingSpeed; }
+        set { m_TargetWalkingSpeed = Mathf.Clamp(value, .1f, m_maxWalkingSpeed); }
+    }
+
+    //Should the agent sample a new goal velocity each episode?
+    //If true, TargetWalkingSpeed will be randomly set between 0.1 and m_maxWalkingSpeed in OnEpisodeBegin()
+    //If false, the goal velocity will be m_maxWalkingSpeed
+    private bool m_RandomizeWalkSpeedEachEpisode;
+
+    //The direction an agent will walk during training.
+    [Header("Target To Walk Towards")] public Transform dynamicTargetPrefab; //Target prefab to use in Dynamic envs
+    public Transform staticTargetPrefab; //Target prefab to use in Static envs
+    private Transform m_Target; //Target the agent will walk towards during training.
 
     [Header("Body Parts")] [Space(10)] public Transform body;
     public Transform leg0Upper;
     public Transform leg3Upper;
     public Transform leg3Lower;
 
-
-    [Header("Orientation")]
-    [Space(10)]
-    public OrientationCubeController orientationCube;
+    OrientationCubeController m_OrientationCube;
+    //The indicator graphic gameobject that points towards the target
+    DirectionIndicator m_DirectionIndicator;
-    [Header("Reward Functions To Use")]
-    [Space(10)]
-    public bool rewardMovingTowardsTarget; // Agent should move towards target
-
-    public bool rewardFacingTarget; // Agent should face the target
-    public bool rewardUseTimePenalty; // Hurry up
-
     [Header("Foot Grounded Visualization")]
     [Space(10)]
     public bool useFootGroundedVisualization;
 
     public override void Initialize()
     {
-        orientationCube.UpdateOrientation(body, target.transform);
+        SetAgentType();
+        m_OrientationCube = GetComponentInChildren<OrientationCubeController>();
+        m_DirectionIndicator = GetComponentInChildren<DirectionIndicator>();
         m_JdController = GetComponent<JointDriveController>();
 
         //Setup each body part
     }
 
     /// <summary>
+    /// Spawns a target prefab at pos
+    /// </summary>
+    /// <param name="prefab"></param>
+    /// <param name="pos"></param>
+    void SpawnTarget(Transform prefab, Vector3 pos)
+    {
+        m_Target = Instantiate(prefab, pos, Quaternion.identity, transform);
+    }
+
+    /// <summary>
+    /// Set up the agent based on the typeOfCrawler
+    /// </summary>
+    void SetAgentType()
+    {
+        var behaviorParams = GetComponent<Unity.MLAgents.Policies.BehaviorParameters>();
+        switch (typeOfCrawler)
+        {
+            case CrawlerAgentBehaviorType.CrawlerDynamic:
+                {
+                    behaviorParams.BehaviorName = "CrawlerDynamic"; //set behavior name
+                    if (crawlerDyModel)
+                        behaviorParams.Model = crawlerDyModel; //assign the model
+                    m_RandomizeWalkSpeedEachEpisode = false; //do not randomize m_TargetWalkingSpeed during training
+                    SpawnTarget(dynamicTargetPrefab, transform.position); //spawn target
+                    break;
+                }
+            case CrawlerAgentBehaviorType.CrawlerDynamicVariableSpeed:
+                {
+                    behaviorParams.BehaviorName = "CrawlerDynamicVariableSpeed"; //set behavior name
+                    if (crawlerDyVSModel)
+                        behaviorParams.Model = crawlerDyVSModel; //assign the model
+                    m_RandomizeWalkSpeedEachEpisode = true; //randomize m_TargetWalkingSpeed during training
+                    SpawnTarget(dynamicTargetPrefab, transform.position); //spawn target
+                    break;
+                }
+            case CrawlerAgentBehaviorType.CrawlerStatic:
+                {
+                    behaviorParams.BehaviorName = "CrawlerStatic"; //set behavior name
+                    if (crawlerStModel)
+                        behaviorParams.Model = crawlerStModel; //assign the model
+                    m_RandomizeWalkSpeedEachEpisode = false; //do not randomize m_TargetWalkingSpeed during training
+                    SpawnTarget(staticTargetPrefab, transform.TransformPoint(new Vector3(0, 0, 1000))); //spawn target
+                    break;
+                }
+            case CrawlerAgentBehaviorType.CrawlerStaticVariableSpeed:
+                {
+                    behaviorParams.BehaviorName = "CrawlerStaticVariableSpeed"; //set behavior name
+                    if (crawlerStVSModel)
+                        behaviorParams.Model = crawlerStVSModel; //assign the model
+                    m_RandomizeWalkSpeedEachEpisode = true; //randomize m_TargetWalkingSpeed during training
+                    SpawnTarget(staticTargetPrefab, transform.TransformPoint(new Vector3(0, 0, 1000))); //spawn target
+                    break;
+                }
+        }
+    }
+
+    /// <summary>
     /// Loop over body parts and reset them to initial conditions.
     /// </summary>
     public override void OnEpisodeBegin()
         }
 
         //Random start rotation to help generalize
-        transform.rotation = Quaternion.Euler(0, Random.Range(0.0f, 360.0f), 0);
+        body.rotation = Quaternion.Euler(0, Random.Range(0.0f, 360.0f), 0);
+
+        UpdateOrientationObjects();
-        orientationCube.UpdateOrientation(body, target.transform);
+        //Set our goal walking speed
+        TargetWalkingSpeed =
+            m_RandomizeWalkSpeedEachEpisode ? Random.Range(0.1f, m_maxWalkingSpeed) : TargetWalkingSpeed;
     }
 
     /// <summary>
     /// </summary>
     public override void CollectObservations(VectorSensor sensor)
     {
+        var cubeForward = m_OrientationCube.transform.forward;
+
+        //velocity we want to match
+        var velGoal = cubeForward * TargetWalkingSpeed;
+        //ragdoll's avg vel
+        var avgVel = GetAvgVelocity();
+
+        //current ragdoll velocity. normalized
+        sensor.AddObservation(Vector3.Distance(velGoal, avgVel));
+        //avg body vel relative to cube
+        sensor.AddObservation(m_OrientationCube.transform.InverseTransformDirection(avgVel));
+        //vel goal relative to cube
+        sensor.AddObservation(m_OrientationCube.transform.InverseTransformDirection(velGoal));
+        //rotation delta
+        sensor.AddObservation(Quaternion.FromToRotation(body.forward, cubeForward));
+
-        sensor.AddObservation(orientationCube.transform.InverseTransformPoint(target.transform.position));
+        sensor.AddObservation(m_OrientationCube.transform.InverseTransformPoint(m_Target.transform.position));
 
         RaycastHit hit;
         float maxRaycastDist = 10;
         }
     }
 
-    /// <summary>
-    /// Agent touched the target
-    /// </summary>
-    public void TouchedTarget()
-    {
-        AddReward(1f);
-    }
-
-
     {
         // The dictionary with all the body parts in it are in the jdController
         var bpDict = m_JdController.bodyPartsDict;
 
     void FixedUpdate()
     {
-        orientationCube.UpdateOrientation(body, target.transform);
+        UpdateOrientationObjects();
 
         // If enabled the feet will light up green when the foot is grounded.
         // This is just a visualization and isn't necessary for function
                 ? groundedMaterial
                 : unGroundedMaterial;
         }
+
+        var cubeForward = m_OrientationCube.transform.forward;
-        if (rewardMovingTowardsTarget)
-        {
-            RewardFunctionMovingTowards();
-        }
+        // a. Match target speed
+        //This reward will approach 1 if it matches perfectly and approach zero as it deviates
+        var matchSpeedReward = GetMatchingVelocityReward(cubeForward * TargetWalkingSpeed, GetAvgVelocity());
-        if (rewardFacingTarget)
-        {
-            RewardFunctionFacingTarget();
-        }
+        // b. Rotation alignment with target direction.
+        //This reward will approach 1 if it faces the target direction perfectly and approach zero as it deviates
+        var lookAtTargetReward = (Vector3.Dot(cubeForward, body.forward) + 1) * .5F;
-        if (rewardUseTimePenalty)
-        {
-            RewardFunctionTimePenalty();
-        }
+        AddReward(matchSpeedReward * lookAtTargetReward);
-    /// Reward moving towards target & Penalize moving away from target.
+    /// Update OrientationCube and DirectionIndicator
-    void RewardFunctionMovingTowards()
+    void UpdateOrientationObjects()
-        var movingTowardsDot = Vector3.Dot(orientationCube.transform.forward,
-            Vector3.ClampMagnitude(m_JdController.bodyPartsDict[body].rb.velocity, maximumWalkingSpeed));
-        if (float.IsNaN(movingTowardsDot))
+        m_OrientationCube.UpdateOrientation(body, m_Target);
+        if (m_DirectionIndicator)
-            throw new ArgumentException(
-                "NaN in movingTowardsDot.\n" +
-                $" orientationCube.transform.forward: {orientationCube.transform.forward}\n" +
-                $" body.velocity: {m_JdController.bodyPartsDict[body].rb.velocity}\n" +
-                $" maximumWalkingSpeed: {maximumWalkingSpeed}"
-            );
+            m_DirectionIndicator.MatchOrientation(m_OrientationCube.transform);
-        AddReward(0.03f * movingTowardsDot);
-    /// Reward facing target & Penalize facing away from target
+    ///Returns the average velocity of all of the body parts
+    ///Using the velocity of the body only has shown to result in more erratic movement from the limbs
+    ///Using the average helps prevent this erratic movement
-    void RewardFunctionFacingTarget()
+    Vector3 GetAvgVelocity()
-        var facingReward = Vector3.Dot(orientationCube.transform.forward, body.forward);
-        if (float.IsNaN(facingReward))
+        Vector3 velSum = Vector3.zero;
+        Vector3 avgVel = Vector3.zero;
+
+        //ALL RBS
+        int numOfRB = 0;
+        foreach (var item in m_JdController.bodyPartsList)
-            throw new ArgumentException(
-                "NaN in movingTowardsDot.\n" +
-                $" orientationCube.transform.forward: {orientationCube.transform.forward}\n" +
-                $" body.forward: {body.forward}"
-            );
+            numOfRB++;
+            velSum += item.rb.velocity;
-        AddReward(0.01f * facingReward);
+
+        avgVel = velSum / numOfRB;
+        return avgVel;
-    /// Existential penalty for time-contrained tasks.
+    /// Normalized value of the difference in actual speed vs goal walking speed.
-    void RewardFunctionTimePenalty()
+    public float GetMatchingVelocityReward(Vector3 velocityGoal, Vector3 actualVelocity)
-        AddReward(-0.001f);
+        //distance between our actual velocity and goal velocity
+        var velDeltaMagnitude = Mathf.Clamp(Vector3.Distance(actualVelocity, velocityGoal), 0, TargetWalkingSpeed);
+
+        //return the value on a declining sigmoid shaped curve that decays from 1 to 0
+        //This reward will approach 1 if it matches perfectly and approach zero as it deviates
+        return Mathf.Pow(1 - Mathf.Pow(velDeltaMagnitude / TargetWalkingSpeed, 2), 2);
+    }
+
+    /// <summary>
+    /// Agent touched the target
+    /// </summary>
+    public void TouchedTarget()
+    {
+        AddReward(1f);
     }
 }

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