Adding the goal conditioning sensors with the new observation specs

.github/workflows/pytest.yml

@@ -61,7 +61,7 @@ jobs:
         pip freeze > pip_versions-${{ matrix.python-version }}.txt
         cat pip_versions-${{ matrix.python-version }}.txt
     - name: Run pytest
-      run: pytest --cov=ml-agents --cov=ml-agents-envs --cov=gym-unity --cov-report html --junitxml=junit/test-results-${{ matrix.python-version }}.xml -p no:warnings
+      run: pytest --cov=ml-agents --cov=ml-agents-envs --cov=gym-unity --cov-report html --junitxml=junit/test-results-${{ matrix.python-version }}.xml -p no:warnings -v
     - name: Upload pytest test results
       uses: actions/upload-artifact@v2
       with:

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

@@ -25,6 +25,7 @@ public override void OnInspectorGUI()
                 EditorGUILayout.PropertyField(so.FindProperty("m_Height"), true);
                 EditorGUILayout.PropertyField(so.FindProperty("m_Grayscale"), true);
                 EditorGUILayout.PropertyField(so.FindProperty("m_ObservationStacks"), true);
+                EditorGUILayout.PropertyField(so.FindProperty("m_ObservationType"), true);
             }
             EditorGUI.EndDisabledGroup();
             EditorGUILayout.PropertyField(so.FindProperty("m_Compression"), true);

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

@@ -0,0 +1,30 @@
+using UnityEditor;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Editor
+{
+    [CustomEditor(typeof(VectorSensorComponent))]
+    [CanEditMultipleObjects]
+    internal class VectorSensorComponentEditor : UnityEditor.Editor
+    {
+        public override void OnInspectorGUI()
+        {
+            var so = serializedObject;
+            so.Update();
+
+            // Drawing the VectorSensorComponent
+
+            EditorGUI.BeginDisabledGroup(!EditorUtilities.CanUpdateModelProperties());
+            {
+                // These fields affect the sensor order or observation size,
+                // So can't be changed at runtime.
+                EditorGUILayout.PropertyField(so.FindProperty("m_SensorName"), true);
+                EditorGUILayout.PropertyField(so.FindProperty("m_ObservationSize"), true);
+                EditorGUILayout.PropertyField(so.FindProperty("m_ObservationType"), true);
+            }
+            EditorGUI.EndDisabledGroup();
+
+            so.ApplyModifiedProperties();
+        }
+    }
+}

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

@@ -35,10 +35,10 @@ static ObservationReflection() {
             "b25fdHlwZRgHIAEoDjIqLmNvbW11bmljYXRvcl9vYmplY3RzLk9ic2VydmF0",
             "aW9uVHlwZVByb3RvEgwKBG5hbWUYCCABKAkaGQoJRmxvYXREYXRhEgwKBGRh",
             "dGEYASADKAJCEgoQb2JzZXJ2YXRpb25fZGF0YSopChRDb21wcmVzc2lvblR5",
-            "cGVQcm90bxIICgROT05FEAASBwoDUE5HEAEqRgoUT2JzZXJ2YXRpb25UeXBl",
-            "UHJvdG8SCwoHREVGQVVMVBAAEggKBEdPQUwQARIKCgZSRVdBUkQQAhILCgdN",
-            "RVNTQUdFEANCJaoCIlVuaXR5Lk1MQWdlbnRzLkNvbW11bmljYXRvck9iamVj",
-            "dHNiBnByb3RvMw=="));
+            "cGVQcm90bxIICgROT05FEAASBwoDUE5HEAEqQAoUT2JzZXJ2YXRpb25UeXBl",
+            "UHJvdG8SCwoHREVGQVVMVBAAEg8KC0dPQUxfU0lHTkFMEAEiBAgCEAIiBAgD",
+            "EANCJaoCIlVuaXR5Lk1MQWdlbnRzLkNvbW11bmljYXRvck9iamVjdHNiBnBy",
+            "b3RvMw=="));
       descriptor = pbr::FileDescriptor.FromGeneratedCode(descriptorData,
           new pbr::FileDescriptor[] { },
           new pbr::GeneratedClrTypeInfo(new[] {typeof(global::Unity.MLAgents.CommunicatorObjects.CompressionTypeProto), typeof(global::Unity.MLAgents.CommunicatorObjects.ObservationTypeProto), }, new pbr::GeneratedClrTypeInfo[] {
@@ -56,9 +56,7 @@ internal enum CompressionTypeProto {
 
   internal enum ObservationTypeProto {
     [pbr::OriginalName("DEFAULT")] Default = 0,
-    [pbr::OriginalName("GOAL")] Goal = 1,
-    [pbr::OriginalName("REWARD")] Reward = 2,
-    [pbr::OriginalName("MESSAGE")] Message = 3,
+    [pbr::OriginalName("GOAL_SIGNAL")] GoalSignal = 1,
   }
 
   #endregion

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

@@ -44,16 +44,17 @@ public SensorCompressionType CompressionType
         /// <param name="grayscale">Whether to convert the generated image to grayscale or keep color.</param>
         /// <param name="name">The name of the camera sensor.</param>
         /// <param name="compression">The compression to apply to the generated image.</param>
+        /// <param name="observationType">The type of observation.</param>
         public CameraSensor(
-            Camera camera, int width, int height, bool grayscale, string name, SensorCompressionType compression)
+            Camera camera, int width, int height, bool grayscale, string name, SensorCompressionType compression, ObservationType observationType = ObservationType.Default)
         {
             m_Camera = camera;
             m_Width = width;
             m_Height = height;
             m_Grayscale = grayscale;
             m_Name = name;
             var channels = grayscale ? 1 : 3;
-            m_ObservationSpec = ObservationSpec.Visual(height, width, channels);
+            m_ObservationSpec = ObservationSpec.Visual(height, width, channels, observationType);
             m_CompressionType = compression;
         }
 

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

@@ -75,6 +75,18 @@ public bool Grayscale
             set { m_Grayscale = value; }
         }
 
+        [HideInInspector, SerializeField]
+        ObservationType m_ObservationType;
+
+        /// <summary>
+        /// The type of the observation.
+        /// </summary>
+        public ObservationType ObservationType
+        {
+            get { return m_ObservationType; }
+            set { m_ObservationType = value; UpdateSensor(); }
+        }
+
         [HideInInspector, SerializeField]
         [Range(1, 50)]
         [Tooltip("Number of camera frames that will be stacked before being fed to the neural network.")]
@@ -108,7 +120,7 @@ public int ObservationStacks
         /// <returns>The created <see cref="CameraSensor"/> object for this component.</returns>
         public override ISensor[] CreateSensors()
         {
-            m_Sensor = new CameraSensor(m_Camera, m_Width, m_Height, Grayscale, m_SensorName, m_Compression);
+            m_Sensor = new CameraSensor(m_Camera, m_Width, m_Height, Grayscale, m_SensorName, m_Compression, m_ObservationType);
 
             if (ObservationStacks != 1)
             {

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

@@ -42,17 +42,7 @@ public enum ObservationType
         /// <summary>
         /// Collected observations contain goal information.
         /// </summary>
-        Goal = 1,
-
-        /// <summary>
-        /// Collected observations contain reward information.
-        /// </summary>
-        Reward = 2,
-
-        /// <summary>
-        /// Collected observations are messages from other agents.
-        /// </summary>
-        Message = 3,
+        GoalSignal = 1,
     }
 
     /// <summary>

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

@@ -21,16 +21,20 @@ public class VectorSensor : ISensor, IBuiltInSensor
         /// </summary>
         /// <param name="observationSize">Number of vector observations.</param>
         /// <param name="name">Name of the sensor.</param>
-        public VectorSensor(int observationSize, string name = null)
+        public VectorSensor(int observationSize, string name = null, ObservationType observationType = ObservationType.Default)
         {
-            if (name == null)
+            if (string.IsNullOrEmpty(name))
             {
                 name = $"VectorSensor_size{observationSize}";
+                if (observationType != ObservationType.Default)
+                {
+                    name += $"_{observationType.ToString()}";
+                }
             }
 
             m_Observations = new List<float>(observationSize);
             m_Name = name;
-            m_ObservationSpec = ObservationSpec.Vector(observationSize);
+            m_ObservationSpec = ObservationSpec.Vector(observationSize, observationType);
         }
 
         /// <inheritdoc/>

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

@@ -0,0 +1,68 @@
+using UnityEngine;
+using UnityEngine.Serialization;
+
+namespace Unity.MLAgents.Sensors
+{
+    /// <summary>
+    /// A SensorComponent that creates a <see cref="VectorSensor"/>.
+    /// </summary>
+    [AddComponentMenu("ML Agents/Vector Sensor", (int)MenuGroup.Sensors)]
+    public class VectorSensorComponent : SensorComponent
+    {
+        /// <summary>
+        /// Name of the generated <see cref="VectorSensor"/> object.
+        /// Note that changing this at runtime does not affect how the Agent sorts the sensors.
+        /// </summary>
+        public string SensorName
+        {
+            get { return m_SensorName; }
+            set { m_SensorName = value; }
+        }
+        [HideInInspector, SerializeField]
+        private string m_SensorName = "VectorSensor";
+
+        /// <summary>
+        /// The number of float observations in the VectorSensor
+        /// </summary>
+        public int ObservationSize
+        {
+            get { return m_ObservationSize; }
+            set { m_ObservationSize = value; }
+        }
+
+        [HideInInspector, SerializeField]
+        int m_ObservationSize;
+
+        [HideInInspector, SerializeField]
+        ObservationType m_ObservationType;
+
+        VectorSensor m_Sensor;
+
+        /// <summary>
+        /// The type of the observation.
+        /// </summary>
+        public ObservationType ObservationType
+        {
+            get { return m_ObservationType; }
+            set { m_ObservationType = value; }
+        }
+
+        /// <summary>
+        /// Creates a VectorSensor.
+        /// </summary>
+        /// <returns></returns>
+        public override ISensor[] CreateSensors()
+        {
+            m_Sensor = new VectorSensor(m_ObservationSize, m_SensorName, m_ObservationType);
+            return new ISensor[] { m_Sensor };
+        }
+
+        /// <summary>
+        /// Returns the underlying VectorSensor
+        /// </summary>
+        public VectorSensor GetSensor()
+        {
+            return m_Sensor;
+        }
+    }
+}

com.unity.ml-agents/Tests/Runtime/Sensor/CameraSensorTest.cs

@@ -37,5 +37,22 @@ public void TestCameraSensor()
                 }
             }
         }
+
+        [Test]
+        public void TestObservationType()
+        {
+            var width = 24;
+            var height = 16;
+            var camera = Camera.main;
+            var sensor = new CameraSensor(camera, width, height, true, "TestCameraSensor", SensorCompressionType.None);
+            var spec = sensor.GetObservationSpec();
+            Assert.AreEqual((int)spec.ObservationType, (int)ObservationType.Default);
+            sensor = new CameraSensor(camera, width, height, true, "TestCameraSensor", SensorCompressionType.None, ObservationType.Default);
+            spec = sensor.GetObservationSpec();
+            Assert.AreEqual((int)spec.ObservationType, (int)ObservationType.Default);
+            sensor = new CameraSensor(camera, width, height, true, "TestCameraSensor", SensorCompressionType.None, ObservationType.GoalSignal);
+            spec = sensor.GetObservationSpec();
+            Assert.AreEqual((int)spec.ObservationType, (int)ObservationType.GoalSignal);
+        }
     }
 }

com.unity.ml-agents/Tests/Runtime/Sensor/VectorSensorTests.cs

@@ -42,6 +42,20 @@ public void TestAddObservationFloat()
             SensorTestHelper.CompareObservation(sensor, new[] { 1.2f });
         }
 
+        [Test]
+        public void TestObservationType()
+        {
+            var sensor = new VectorSensor(1);
+            var spec = sensor.GetObservationSpec();
+            Assert.AreEqual((int)spec.ObservationType, (int)ObservationType.Default);
+            sensor = new VectorSensor(1, observationType: ObservationType.Default);
+            spec = sensor.GetObservationSpec();
+            Assert.AreEqual((int)spec.ObservationType, (int)ObservationType.Default);
+            sensor = new VectorSensor(1, observationType: ObservationType.GoalSignal);
+            spec = sensor.GetObservationSpec();
+            Assert.AreEqual((int)spec.ObservationType, (int)ObservationType.GoalSignal);
+        }
+
         [Test]
         public void TestAddObservationInt()
         {

ml-agents-envs/mlagents_envs/base_env.py

@@ -486,11 +486,7 @@ class ObservationType(Enum):
     # Observation information is generic.
     DEFAULT = 0
     # Observation contains goal information for current task.
-    GOAL = 1
-    # Observation contains reward information for current task.
-    REWARD = 2
-    # Observation contains a message from another agent.
-    MESSAGE = 3
+    GOAL_SIGNAL = 1
 
 
 class ObservationSpec(NamedTuple):

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

@@ -64,13 +64,9 @@ class ObservationTypeProto(builtin___int):
     @classmethod
     def items(cls) -> typing___List[typing___Tuple[builtin___str, 'ObservationTypeProto']]: ...
     DEFAULT = typing___cast('ObservationTypeProto', 0)
-    GOAL = typing___cast('ObservationTypeProto', 1)
-    REWARD = typing___cast('ObservationTypeProto', 2)
-    MESSAGE = typing___cast('ObservationTypeProto', 3)
+    GOAL_SIGNAL = typing___cast('ObservationTypeProto', 1)
 DEFAULT = typing___cast('ObservationTypeProto', 0)
-GOAL = typing___cast('ObservationTypeProto', 1)
-REWARD = typing___cast('ObservationTypeProto', 2)
-MESSAGE = typing___cast('ObservationTypeProto', 3)
+GOAL_SIGNAL = typing___cast('ObservationTypeProto', 1)
 
 class ObservationProto(google___protobuf___message___Message):
     DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...

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

@@ -121,7 +121,7 @@ def _make_observation_specs(self) -> List[ObservationSpec]:
                     obs_spec[i] = ObservationSpec(
                         shape=obs_spec[i].shape,
                         dimension_property=obs_spec[i].dimension_property,
-                        observation_type=ObservationType.GOAL,
+                        observation_type=ObservationType.GOAL_SIGNAL,
                         name=obs_spec[i].name,
                     )
         return obs_spec

ml-agents/mlagents/trainers/torch/networks.py

@@ -60,7 +60,7 @@ def __init__(
         self._total_goal_enc_size = 0
         self._goal_processor_indices: List[int] = []
         for i in range(len(observation_specs)):
-            if observation_specs[i].observation_type == ObservationType.GOAL:
+            if observation_specs[i].observation_type == ObservationType.GOAL_SIGNAL:
                 self._total_goal_enc_size += self.embedding_sizes[i]
                 self._goal_processor_indices.append(i)