1 to 1 Brain to Agent (#2729)

* 1 to 1 Brain to Agent
This is a work in progess
In this PR :
 - Deleted all Brain Objects
 - Moved the BrainParameters into the Agent
 - Gave the Agent a Heuristic method (see Balance Ball for example)
 - Modified the Communicator and ModelRunner : Put can only take one agent at a time
 - Made the IBrain Interface with RequestDecision and DecideAction method

No changes made to Python
[Design Doc](https://docs.google.com/document/d/1hBhBxZ9lepGF4H6fc6Hu6AW7UwOmnyX3trmgI3HpOmo/edit#)

* Removing editorconfig

* Updating BallanceBall scene

* grammar mistake

* Clearing the Agents of the Model runner

* Added Documentation on IBrain

* Modified comments on GiveModel

* Introduced a factory

* Split Learning Brain in two

* Changes to walljump

* Fixing the Unit tests

* Renaming the Brain to Policy

* Heuristic now has priority over training

* Edited code comments

* Fixing bugs

* Develop one to one scene edits...

UnitySDK/Assets/ML-Agents/Editor/AgentEditor.cs

 using UnityEngine;
 using UnityEditor;
+using Barracuda;
-/*
- This code is meant to modify the behavior of the inspector on Brain Components.
- Depending on the type of brain that is used, the available fields will be modified in the inspector accordingly.
-*/
+    /*
+     This code is meant to modify the behavior of the inspector on Agent Components.
+    */
     [CustomEditor(typeof(Agent), true)]
     [CanEditMultipleObjects]
     public class AgentEditor : Editor
             var serializedAgent = serializedObject;
             serializedAgent.Update();
 
-            var brain = serializedAgent.FindProperty("brain");
             var actionsPerDecision = serializedAgent.FindProperty(
                 "agentParameters.numberOfActionsBetweenDecisions");
             var maxSteps = serializedAgent.FindProperty(
             var isOdd = serializedAgent.FindProperty(
                 "agentParameters.onDemandDecision");
 
-            EditorGUILayout.PropertyField(brain);
 
 
             EditorGUILayout.PropertyField(

UnitySDK/Assets/ML-Agents/Editor/BrainParametersDrawer.cs

         /// <inheritdoc />
         public override float GetPropertyHeight(SerializedProperty property, GUIContent label)
         {
-            if (property.isExpanded)
-            {
-                return k_LineHeight +
-                    GetHeightDrawVectorObservation() +
-                    GetHeightDrawVectorAction(property) +
-                    GetHeightDrawVectorActionDescriptions(property);
-            }
-            return k_LineHeight;
+            return GetHeightDrawVectorObservation() +
+                GetHeightDrawVectorAction(property);
+
+
-            property.isExpanded = EditorGUI.Foldout(position, property.isExpanded, label);
-            position.y += k_LineHeight;
-            if (property.isExpanded)
-            {
-                EditorGUI.BeginProperty(position, label, property);
-                EditorGUI.indentLevel++;
+            EditorGUI.BeginProperty(position, label, property);
+            EditorGUI.indentLevel++;
-                // Vector Observations
-                DrawVectorObservation(position, property);
-                position.y += GetHeightDrawVectorObservation();
+            // Vector Observations
+            DrawVectorObservation(position, property);
+            position.y += GetHeightDrawVectorObservation();
-                // Vector Action
-                DrawVectorAction(position, property);
-                position.y += GetHeightDrawVectorAction(property);
+            // Vector Action
+            DrawVectorAction(position, property);
+            position.y += GetHeightDrawVectorAction(property);
-                // Vector Action Descriptions
-                DrawVectorActionDescriptions(position, property);
-                position.y += GetHeightDrawVectorActionDescriptions(property);
-                EditorGUI.EndProperty();
-            }
+            EditorGUI.EndProperty();
             EditorGUI.indentLevel = indent;
         }
 
                 actionSize += 1;
             }
             return actionSize * k_LineHeight;
-        }
-
-        /// <summary>
-        /// Draws the Vector Actions descriptions for the Brain Parameters
-        /// </summary>
-        /// <param name="position">Rectangle on the screen to use for the property GUI.</param>
-        /// <param name="property">The SerializedProperty of the BrainParameters
-        /// to make the custom GUI for.</param>
-        private static void DrawVectorActionDescriptions(Rect position, SerializedProperty property)
-        {
-            var bpVectorActionType = property.FindPropertyRelative(k_ActionTypePropName);
-            var vecActionSize = property.FindPropertyRelative(k_ActionSizePropName);
-            var numberOfDescriptions = 0;
-            if (bpVectorActionType.enumValueIndex == 1)
-            {
-                numberOfDescriptions = vecActionSize.GetArrayElementAtIndex(0).intValue;
-            }
-            else
-            {
-                numberOfDescriptions = vecActionSize.arraySize;
-            }
-
-            EditorGUI.indentLevel++;
-            var vecActionDescriptions =
-                property.FindPropertyRelative(k_ActionDescriptionPropName);
-            vecActionDescriptions.arraySize = numberOfDescriptions;
-            if (bpVectorActionType.enumValueIndex == 1)
-            {
-                //Continuous case :
-                EditorGUI.PropertyField(
-                    position,
-                    vecActionDescriptions,
-                    new GUIContent("Action Descriptions",
-                        "A list of strings used to name the available actionsm for the Brain."),
-                    true);
-                position.y += k_LineHeight;
-            }
-            else
-            {
-                // Discrete case :
-                EditorGUI.PropertyField(
-                    position,
-                    vecActionDescriptions,
-                    new GUIContent("Branch Descriptions",
-                        "A list of strings used to name the available branches for the Brain."),
-                    true);
-                position.y += k_LineHeight;
-            }
-        }
-
-        /// <summary>
-        /// The Height required to draw the Action Descriptions
-        /// </summary>
-        /// <returns>The height of the drawer of the Action Descriptions </returns>
-        private static float GetHeightDrawVectorActionDescriptions(SerializedProperty property)
-        {
-            var descriptionSize = 1;
-            if (property.FindPropertyRelative(k_ActionDescriptionPropName).isExpanded)
-            {
-                var descriptions = property.FindPropertyRelative(k_ActionDescriptionPropName);
-                descriptionSize += descriptions.arraySize + 1;
-            }
-            return descriptionSize * k_LineHeight;
         }
     }
 }

UnitySDK/Assets/ML-Agents/Editor/Tests/EditModeTestInternalBrainTensorGenerator.cs

 {
     public class EditModeTestInternalBrainTensorGenerator
     {
-        private class TestAgent : Agent
-        {
-        }
-
         private static IEnumerable<Agent> GetFakeAgentInfos()
         {
             var goA = new GameObject("goA");
-                stackedVectorObservation = new[] {1f, 2f, 3f}.ToList(),
+                stackedVectorObservation = new[] { 1f, 2f, 3f }.ToList(),
-                storedVectorActions = new[] {1f, 2f},
+                storedVectorActions = new[] { 1f, 2f },
                 actionMasks = null
             };
             var goB = new GameObject("goB");
-                stackedVectorObservation = new[] {4f, 5f, 6f}.ToList(),
-                memories = new[] {1f, 1f, 1f}.ToList(),
-                storedVectorActions = new[] {3f, 4f},
-                actionMasks = new[] {true, false, false, false, false},
+                stackedVectorObservation = new[] { 4f, 5f, 6f }.ToList(),
+                memories = new[] { 1f, 1f, 1f }.ToList(),
+                storedVectorActions = new[] { 3f, 4f },
+                actionMasks = new[] { true, false, false, false, false },
-            return new List<Agent> {agentA, agentB};
+            return new List<Agent> { agentA, agentB };
         }
 
         [Test]
         {
             var inputTensor = new TensorProxy
             {
-                shape = new long[] {2, 3}
+                shape = new long[] { 2, 3 }
             };
             const int batchSize = 4;
             var agentInfos = GetFakeAgentInfos();
         {
             var inputTensor = new TensorProxy
             {
-                shape = new long[] {2, 5}
+                shape = new long[] { 2, 5 }
             };
             const int batchSize = 4;
             var agentInfos = GetFakeAgentInfos();
         {
             var inputTensor = new TensorProxy
             {
-                shape = new long[] {2, 2},
+                shape = new long[] { 2, 2 },
                 valueType = TensorProxy.TensorType.Integer
             };
             const int batchSize = 4;
         {
             var inputTensor = new TensorProxy
             {
-                shape = new long[] {2, 5},
+                shape = new long[] { 2, 5 },
                 valueType = TensorProxy.TensorType.FloatingPoint
             };
             const int batchSize = 4;

UnitySDK/Assets/ML-Agents/Editor/Tests/MLAgentsEditModeTest.cs

         public override void CollectObservations()
         {
             collectObservationsCalls += 1;
+            AddVectorObs(0f);
         }
 
         public override void AgentAction(float[] vectorAction, string textAction)
         public override void AgentOnDone()
         {
             agentOnDoneCalls += 1;
+        }
+
+        public override float[] Heuristic()
+        {
+            return new float[0];
         }
     }
 
             sensorName = n;
         }
 
-        public int[] GetFloatObservationShape() {
-            return new[] {1};
+        public int[] GetFloatObservationShape()
+        {
+            return new[] { 1 };
         }
 
         public void WriteToTensor(TensorProxy tensorProxy, int agentIndex) { }
         }
     }
 
-    // This is an empty class for testing the behavior of agents and academy
-    // It is left empty because we are not testing any brain behavior
-    public class TestBrain : Brain
-    {
-        public int numberOfCallsToInitialize;
-        public int numberOfCallsToDecideAction;
-        public static TestBrain Instantiate()
-        {
-            return CreateInstance<TestBrain>();
-        }
-
-        protected override void Initialize()
-        {
-            numberOfCallsToInitialize++;
-        }
-
-        protected override void DecideAction()
-        {
-            numberOfCallsToDecideAction++;
-            m_Agents.Clear();
-        }
-    }
-
-
     public class EditModeTestGeneration
     {
         [Test]
             //This will call the method even though it is private
             var academyInitializeMethod = typeof(Academy).GetMethod("InitializeEnvironment",
                 BindingFlags.Instance | BindingFlags.NonPublic);
-            academyInitializeMethod?.Invoke(aca, new object[] {});
+            academyInitializeMethod?.Invoke(aca, new object[] { });
             Assert.AreEqual(1, aca.initializeAcademyCalls);
             Assert.AreEqual(0, aca.GetEpisodeCount());
             Assert.AreEqual(0, aca.GetStepCount());
             acaGo.AddComponent<TestAcademy>();
             var aca = acaGo.GetComponent<TestAcademy>();
             aca.resetParameters = new ResetParameters();
-            var brain = TestBrain.Instantiate();
-            brain.brainParameters = new BrainParameters();
-            brain.brainParameters.vectorObservationSize = 0;
-            agent1.GiveBrain(brain);
-            agent2.GiveBrain(brain);
 
             Assert.AreEqual(false, agent1.IsDone());
             Assert.AreEqual(false, agent2.IsDone());
 
 
             agentEnableMethod?.Invoke(agent2, new object[] { aca });
-            academyInitializeMethod?.Invoke(aca, new object[] {});
+            academyInitializeMethod?.Invoke(aca, new object[] { });
             agentEnableMethod?.Invoke(agent1, new object[] { aca });
 
             Assert.AreEqual(false, agent1.IsDone());
             aca.resetParameters = new ResetParameters();
             var academyInitializeMethod = typeof(Academy).GetMethod("InitializeEnvironment",
                 BindingFlags.Instance | BindingFlags.NonPublic);
-            academyInitializeMethod?.Invoke(aca, new object[] {});
+            academyInitializeMethod?.Invoke(aca, new object[] { });
 
             var academyStepMethod = typeof(Academy).GetMethod("EnvironmentStep",
                 BindingFlags.Instance | BindingFlags.NonPublic);
                 {
                     numberReset += 1;
                 }
-                academyStepMethod?.Invoke(aca, new object[] {});
+                academyStepMethod?.Invoke(aca, new object[] { });
             }
         }
 
             acaGo.AddComponent<TestAcademy>();
             var aca = acaGo.GetComponent<TestAcademy>();
             aca.resetParameters = new ResetParameters();
-            var brain = TestBrain.Instantiate();
 
 
             var agentEnableMethod = typeof(Agent).GetMethod(
 
             agent1.agentParameters = new AgentParameters();
             agent2.agentParameters = new AgentParameters();
-            brain.brainParameters = new BrainParameters();
             // We use event based so the agent will now try to send anything to the brain
             agent1.agentParameters.onDemandDecision = false;
             agent1.agentParameters.numberOfActionsBetweenDecisions = 2;
-            brain.brainParameters.vectorObservationSize = 0;
-            agent1.GiveBrain(brain);
-            agent2.GiveBrain(brain);
-            academyInitializeMethod?.Invoke(aca, new object[] {});
+            academyInitializeMethod?.Invoke(aca, new object[] { });
 
             var academyStepMethod = typeof(Academy).GetMethod(
                 "EnvironmentStep", BindingFlags.Instance | BindingFlags.NonPublic);
                     requestAction += 1;
                     agent2.RequestAction();
                 }
-                academyStepMethod?.Invoke(aca, new object[] {});
+                academyStepMethod?.Invoke(aca, new object[] { });
             }
         }
     }
             aca.resetParameters = new ResetParameters();
             var academyInitializeMethod = typeof(Academy).GetMethod(
                 "InitializeEnvironment", BindingFlags.Instance | BindingFlags.NonPublic);
-            academyInitializeMethod?.Invoke(aca, new object[] {});
+            academyInitializeMethod?.Invoke(aca, new object[] { });
 
             var academyStepMethod = typeof(Academy).GetMethod(
                 "EnvironmentStep", BindingFlags.Instance | BindingFlags.NonPublic);
                 }
 
                 stepsSinceReset += 1;
-                academyStepMethod.Invoke(aca, new object[] {});
+                academyStepMethod.Invoke(aca, new object[] { });
             }
         }
 
             acaGo.AddComponent<TestAcademy>();
             var aca = acaGo.GetComponent<TestAcademy>();
             aca.resetParameters = new ResetParameters();
-            var brain = TestBrain.Instantiate();
 
 
             var agentEnableMethod = typeof(Agent).GetMethod(
 
             agent1.agentParameters = new AgentParameters();
             agent2.agentParameters = new AgentParameters();
-            brain.brainParameters = new BrainParameters();
-            // agent2 will request decisions only when RequestDecision is called
-            brain.brainParameters.vectorObservationSize = 0;
-            agent1.GiveBrain(brain);
-            agent2.GiveBrain(brain);
-            academyInitializeMethod?.Invoke(aca, new object[] {});
+            academyInitializeMethod?.Invoke(aca, new object[] { });
 
             var numberAgent1Reset = 0;
             var numberAgent2Reset = 0;
                 agent2StepSinceReset += 1;
                 //Agent 1 is only initialized at step 2
                 if (i < 2)
-                {}
-                academyStepMethod?.Invoke(aca, new object[] {});
+                { }
+                academyStepMethod?.Invoke(aca, new object[] { });
             }
         }
     }
             acaGo.AddComponent<TestAcademy>();
             var aca = acaGo.GetComponent<TestAcademy>();
             aca.resetParameters = new ResetParameters();
-            var brain = TestBrain.Instantiate();
 
 
             var agentEnableMethod = typeof(Agent).GetMethod(
 
             agent1.agentParameters = new AgentParameters();
             agent2.agentParameters = new AgentParameters();
-            brain.brainParameters = new BrainParameters();
             // We use event based so the agent will now try to send anything to the brain
             agent1.agentParameters.onDemandDecision = false;
             // agent1 will take an action at every step and request a decision every steps
             //Here we specify that the agent does not reset when done
             agent1.agentParameters.resetOnDone = false;
             agent2.agentParameters.resetOnDone = false;
-            brain.brainParameters.vectorObservationSize = 0;
-            agent1.GiveBrain(brain);
-            agent2.GiveBrain(brain);
-            academyInitializeMethod?.Invoke(aca, new object[] {});
+            academyInitializeMethod?.Invoke(aca, new object[] { });
             agentEnableMethod?.Invoke(agent1, new object[] { aca });
 
             var agent1ResetOnDone = 0;
                 }
 
 
-                academyStepMethod?.Invoke(aca, new object[] {});
+                academyStepMethod?.Invoke(aca, new object[] { });
             }
         }
 
             acaGo.AddComponent<TestAcademy>();
             var aca = acaGo.GetComponent<TestAcademy>();
             aca.resetParameters = new ResetParameters();
-            var brain = TestBrain.Instantiate();
 
 
             var agentEnableMethod = typeof(Agent).GetMethod(
 
             agent1.agentParameters = new AgentParameters();
             agent2.agentParameters = new AgentParameters();
-            brain.brainParameters = new BrainParameters();
             // We use event based so the agent will now try to send anything to the brain
             agent1.agentParameters.onDemandDecision = false;
             agent1.agentParameters.numberOfActionsBetweenDecisions = 3;
             agent1.agentParameters.maxStep = 20;
-            brain.brainParameters.vectorObservationSize = 0;
-            agent1.GiveBrain(brain);
-            agent2.GiveBrain(brain);
-            academyInitializeMethod?.Invoke(aca, new object[] {});
+            academyInitializeMethod?.Invoke(aca, new object[] { });
             agentEnableMethod?.Invoke(agent1, new object[] { aca });
 
 
                 Assert.LessOrEqual(Mathf.Abs(i * 0.1f - agent2.GetCumulativeReward()), 0.05f);
 
 
-                academyStepMethod?.Invoke(aca, new object[] {});
+                academyStepMethod?.Invoke(aca, new object[] { });
                 agent1.AddReward(10f);
 
                 if ((i % 21 == 0) && (i > 0))

UnitySDK/Assets/ML-Agents/Examples/3DBall/Prefabs/3DBall.prefab

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+    vectorActionSpaceType: 1
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-    agentCameras: []
-    agentRenderTextures: []
     maxStep: 5000
     resetOnDone: 1
     onDemandDecision: 0

UnitySDK/Assets/ML-Agents/Examples/3DBall/Prefabs/3DBallHardNew.prefab

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UnitySDK/Assets/ML-Agents/Examples/3DBall/Scenes/3DBall.unity

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

 
     public override void AgentAction(float[] vectorAction, string textAction)
     {
-        if (brain.brainParameters.vectorActionSpaceType == SpaceType.Continuous)
+        var actionZ = 2f * Mathf.Clamp(vectorAction[0], -1f, 1f);
+        var actionX = 2f * Mathf.Clamp(vectorAction[1], -1f, 1f);
+
+        if ((gameObject.transform.rotation.z < 0.25f && actionZ > 0f) ||
+            (gameObject.transform.rotation.z > -0.25f && actionZ < 0f))
-            var actionZ = 2f * Mathf.Clamp(vectorAction[0], -1f, 1f);
-            var actionX = 2f * Mathf.Clamp(vectorAction[1], -1f, 1f);
+            gameObject.transform.Rotate(new Vector3(0, 0, 1), actionZ);
+        }
-            if ((gameObject.transform.rotation.z < 0.25f && actionZ > 0f) ||
-                (gameObject.transform.rotation.z > -0.25f && actionZ < 0f))
-            {
-                gameObject.transform.Rotate(new Vector3(0, 0, 1), actionZ);
-            }
-
-            if ((gameObject.transform.rotation.x < 0.25f && actionX > 0f) ||
-                (gameObject.transform.rotation.x > -0.25f && actionX < 0f))
-            {
-                gameObject.transform.Rotate(new Vector3(1, 0, 0), actionX);
-            }
+        if ((gameObject.transform.rotation.x < 0.25f && actionX > 0f) ||
+            (gameObject.transform.rotation.x > -0.25f && actionX < 0f))
+        {
+            gameObject.transform.Rotate(new Vector3(1, 0, 0), actionX);
         }
         if ((ball.transform.position.y - gameObject.transform.position.y) < -2f ||
             Mathf.Abs(ball.transform.position.x - gameObject.transform.position.x) > 3f ||
             + gameObject.transform.position;
         //Reset the parameters when the Agent is reset.
         SetResetParameters();
+    }
+
+    public override float[] Heuristic()
+    {
+        var action = new float[2];
+
+        action[0] = -Input.GetAxis("Horizontal");
+        action[1] = Input.GetAxis("Vertical");
+        return action;
     }
 
     public void SetBall()

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

 
     public override void AgentAction(float[] vectorAction, string textAction)
     {
-        if (brain.brainParameters.vectorActionSpaceType == SpaceType.Continuous)
+        var actionZ = 2f * Mathf.Clamp(vectorAction[0], -1f, 1f);
+        var actionX = 2f * Mathf.Clamp(vectorAction[1], -1f, 1f);
+
+        if ((gameObject.transform.rotation.z < 0.25f && actionZ > 0f) ||
+            (gameObject.transform.rotation.z > -0.25f && actionZ < 0f))
-            var actionZ = 2f * Mathf.Clamp(vectorAction[0], -1f, 1f);
-            var actionX = 2f * Mathf.Clamp(vectorAction[1], -1f, 1f);
+            gameObject.transform.Rotate(new Vector3(0, 0, 1), actionZ);
+        }
-            if ((gameObject.transform.rotation.z < 0.25f && actionZ > 0f) ||
-                (gameObject.transform.rotation.z > -0.25f && actionZ < 0f))
-            {
-                gameObject.transform.Rotate(new Vector3(0, 0, 1), actionZ);
-            }
-
-            if ((gameObject.transform.rotation.x < 0.25f && actionX > 0f) ||
-                (gameObject.transform.rotation.x > -0.25f && actionX < 0f))
-            {
-                gameObject.transform.Rotate(new Vector3(1, 0, 0), actionX);
-            }
+        if ((gameObject.transform.rotation.x < 0.25f && actionX > 0f) ||
+            (gameObject.transform.rotation.x > -0.25f && actionX < 0f))
+        {
+            gameObject.transform.Rotate(new Vector3(1, 0, 0), actionX);
         }
         if ((ball.transform.position.y - gameObject.transform.position.y) < -2f ||
             Mathf.Abs(ball.transform.position.x - gameObject.transform.position.x) > 3f ||

UnitySDK/Assets/ML-Agents/Examples/Basic/Prefabs/Basic.prefab

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UnitySDK/Assets/ML-Agents/Examples/Basic/Scripts/BasicAgent.cs

         largeGoal.transform.position = new Vector3(m_LargeGoalPosition - 10f, 0f, 0f);
     }
 
+    public override float[] Heuristic()
+    {
+        if (Input.GetKey(KeyCode.D))
+        {
+            return new float[] { 2 };
+        }
+        if (Input.GetKey(KeyCode.A))
+        {
+            return new float[] { 1 };
+        }
+        return new float[] { 0 };
+    }
+
     public override void AgentOnDone()
     {
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UnitySDK/Assets/ML-Agents/Examples/Bouncer/Prefabs/Environment.prefab

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

         }
     }
 
+    public override float[] Heuristic()
+    {
+        var action = new float[3];
+
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+        action[1] = Input.GetKey(KeyCode.Space) ? 1.0f : 0.0f;
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+        return action;
+    }
+
     private void Update()
     {
         if (m_LookDir.magnitude > float.Epsilon)

UnitySDK/Assets/ML-Agents/Examples/Crawler/Prefabs/DynamicPlatform.prefab

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