Merge pull request #1764 from Unity-Technologies/release-v0.7

Release v0.7 into master

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

                 EditorGUILayout.PropertyField(
                     actionsPerDecision,
                     new GUIContent(
-                        "Decision Frequency",
+                        "Decision Interval",
                         "The agent will automatically request a decision every X" +
                         " steps and perform an action at every step."));
                 actionsPerDecision.intValue = Mathf.Max(1, actionsPerDecision.intValue);

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

     public class LearningBrainEditor : BrainEditor
     {
         private const string ModelPropName = "model";
+        private const string InferenceDevicePropName = "inferenceDevice";
         private const float TimeBetweenModelReloads = 2f;
         // Time since the last reload of the model
         private float _timeSinceModelReload;
             serializedBrain.Update(); 
             var tfGraphModel = serializedBrain.FindProperty(ModelPropName);
             EditorGUILayout.ObjectField(tfGraphModel);
+            var inferenceDevice = serializedBrain.FindProperty(InferenceDevicePropName);
+            EditorGUILayout.PropertyField(inferenceDevice);
             serializedBrain.ApplyModifiedProperties();
             if (EditorGUI.EndChangeCheck())
             {

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

             var inputTensor = new Tensor()
             {
                 Shape = new long[] {2, 2},
-                ValueType = Tensor.TensorType.FloatingPoint
+                ValueType = Tensor.TensorType.Integer
                 
             };
             var batchSize = 4;
-            Assert.Catch<NotImplementedException>(
-                () => generator.Generate(inputTensor, batchSize, agentInfos));
-            inputTensor.ValueType = Tensor.TensorType.Integer;
             generator.Generate(inputTensor, batchSize, agentInfos);
             Assert.IsNotNull(inputTensor.Data as int[,]);
             Assert.AreEqual((inputTensor.Data as int[,])[0, 0], 1);

UnitySDK/Assets/ML-Agents/Examples/3DBall/Brains/3DBallHardLearning.asset

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

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UnitySDK/Assets/ML-Agents/Examples/Soccer/Brains/StrikerLearning.asset

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UnitySDK/Assets/ML-Agents/Examples/Soccer/Prefabs/SoccerFieldTwos.prefab

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UnitySDK/Assets/ML-Agents/Examples/Template/Scene.unity

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+    broadcastingBrains: []
+    _brainsToControl: []
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-  defaultResetParameters: []
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UnitySDK/Assets/ML-Agents/Examples/Tennis/Brains/TennisLearning.asset

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+  inferenceDevice: 0

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

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     maxStep: 5000

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

     public override void InitializeAgent()
     {
         agentRb = GetComponent<Rigidbody>();
-        ballRb = GetComponent<Rigidbody>();
+        ballRb = ball.GetComponent<Rigidbody>();
         var canvas = GameObject.Find(CanvasName);
         GameObject scoreBoard;
         if (invertX)

UnitySDK/Assets/ML-Agents/Examples/Walker/Brains/WalkerLearning.asset

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UnitySDK/Assets/ML-Agents/Examples/WallJump/Brains/BigWallJumpLearning.asset

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UnitySDK/Assets/ML-Agents/Examples/WallJump/Brains/SmallWallJumpLearning.asset

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UnitySDK/Assets/ML-Agents/Plugins/ProtoBuffer/Grpc.Core.dll.meta

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UnitySDK/Assets/ML-Agents/Scripts/Academy.cs

         [SerializeField] 
         public BroadcastHub broadcastHub = new BroadcastHub();
         
-        private const string kApiVersion = "API-6";
+        private const string kApiVersion = "API-7";
+
+        /// Temporary storage for global gravity value
+        /// Used to restore oringal value when deriving Academy modifies it 
+        private Vector3 originalGravity;
+
+        /// Temporary storage for global fixedDeltaTime value
+        /// Used to restore oringal value when deriving Academy modifies it 
+        private float originalFixedDeltaTime;
+        
+        /// Temporary storage for global maximumDeltaTime value
+        /// Used to restore oringal value when deriving Academy modifies it 
+        private float originalMaximumDeltaTime;
 
         // Fields provided in the Inspector
 
         /// </summary>
         private void InitializeEnvironment()
         {
+            originalGravity = Physics.gravity;
+            originalFixedDeltaTime = Time.fixedDeltaTime;
+            originalMaximumDeltaTime = Time.maximumDeltaTime;
+            
             InitializeAcademy();
             Communicator communicator = null;
 
         void FixedUpdate()
         {
             EnvironmentStep();
+        }
+
+        /// <summary>
+        /// Cleanup function
+        /// </summary>
+        protected virtual void OnDestroy()
+        {
+            Physics.gravity = originalGravity;
+            Time.fixedDeltaTime = originalFixedDeltaTime;
+            Time.maximumDeltaTime = originalMaximumDeltaTime;
         }
     }
 }

UnitySDK/Assets/ML-Agents/Scripts/Agent.cs

         }
 
         /// <summary>
-        /// Adds a float array observation to the vector observations of the agent.
-        /// Increases the size of the agents vector observation by size of array.
-        /// </summary>
-        /// <param name="observation">Observation.</param>
-        protected void AddVectorObs(float[] observation)
-        {
-            info.vectorObservation.AddRange(observation);
-        }
-
-        /// <summary>
-        /// Adds a float list observation to the vector observations of the agent.
-        /// Increases the size of the agents vector observation by size of list.
+        /// Adds a collection of float observations to the vector observations of the agent.
+        /// Increases the size of the agents vector observation by size of the collection.
-        protected void AddVectorObs(List<float> observation)
+        protected void AddVectorObs(IEnumerable<float> observation)
         {
             info.vectorObservation.AddRange(observation);
         }
         public void UpdateMemoriesAction(List<float> memories)
         {
             action.memories = memories;
+        }
+        
+        public void AppendMemoriesAction(List<float> memories)
+        {
+            action.memories.AddRange(memories);
         }
 
         /// <summary>

UnitySDK/Assets/ML-Agents/Scripts/CommunicatorObjects/UnityToExternalGrpc.cs

 //     Generated by the protocol buffer compiler.  DO NOT EDIT!
 //     source: mlagents/envs/communicator_objects/unity_to_external.proto
 // </auto-generated>
+
+# if UNITY_EDITOR || UNITY_STANDALONE_WIN || UNITY_STANDALONE_OSX || UNITY_STANDALONE_LINUX
+
 #pragma warning disable 1591
 #region Designer generated code
 
   }
 }
 #endregion
+
+#endif 
+

UnitySDK/Assets/ML-Agents/Scripts/InferenceBrain/ApplierImpl.cs

 using System.Collections.Generic;
 using MLAgents.InferenceBrain.Utils;
+using UnityEngine;
 
 namespace MLAgents.InferenceBrain
 {
         public void Apply(Tensor tensor, Dictionary<Agent, AgentInfo> agentInfo)
         {
             var tensorDataAction = tensor.Data as float[,];
-            var actionSize = tensor.Shape[1];
+            var actionSize = tensor.Shape[tensor.Shape.Length - 1];
             var agentIndex = 0;
             foreach (var agent in agentInfo.Keys)
             {
             }
         }
     }
+    
+    public class BarracudaMemoryOutputApplier : TensorApplier.Applier
+    {
+        private bool firstHalf = true;
+
+        public BarracudaMemoryOutputApplier(bool firstHalf)
+        {
+            this.firstHalf = firstHalf;
+        }
+        
+        public void Apply(Tensor tensor, Dictionary<Agent, AgentInfo> agentInfo)
+        {
+            var tensorDataMemory = tensor.Data as float[,];
+            var agentIndex = 0;
+            var memorySize = tensor.Shape[tensor.Shape.Length - 1];
+            foreach (var agent in agentInfo.Keys)
+            {
+                var memory = new List<float>();
+                for (var j = 0; j < memorySize; j++)
+                {
+                    memory.Add(tensorDataMemory[agentIndex, j]);
+                }
+
+                if (firstHalf)
+                {
+                    agent.UpdateMemoriesAction(memory);
+                }
+                else
+                {
+                    agent.AppendMemoriesAction(memory);
+                }
+                agentIndex++;
+            }
+        }
+    }
 
     /// <summary>
     /// The Applier for the Memory output tensor. Tensor is assumed to contain the new
         {
             var tensorDataMemory = tensor.Data as float[,];
             var agentIndex = 0;
-            var memorySize = tensor.Shape[1];
+            var memorySize = tensor.Shape[tensor.Shape.Length - 1];
             foreach (var agent in agentInfo.Keys)
             {
                 var memory = new List<float>();

UnitySDK/Assets/ML-Agents/Scripts/InferenceBrain/GeneratorImpl.cs

     {
         public void Generate(Tensor tensor, int batchSize, Dictionary<Agent, AgentInfo> agentInfo)
         {
-            var shapeSecondAxis = tensor.Shape[1];
+            var shapeSecondAxis = tensor.Shape[tensor.Shape.Length - 1];
             tensor.Shape[0] = batchSize;
             if (tensor.ValueType == Tensor.TensorType.FloatingPoint)
             {
         public void Generate(Tensor tensor, int batchSize, Dictionary<Agent, AgentInfo> agentInfo)
         {
             tensor.Shape[0] = batchSize;
-            var vecObsSizeT = tensor.Shape[1];
+            var vecObsSizeT = tensor.Shape[tensor.Shape.Length - 1];
             tensor.Data = new float[batchSize, vecObsSizeT];
             var agentIndex = 0;
             foreach (var agent in agentInfo.Keys)
         public void Generate(Tensor tensor, int batchSize, Dictionary<Agent, AgentInfo> agentInfo)
         {
             tensor.Shape[0] = batchSize;
-            var memorySize = tensor.Shape[1];
+            var memorySize = tensor.Shape[tensor.Shape.Length - 1];
             tensor.Data = new float[batchSize, memorySize];
             var agentIndex = 0;
             foreach (var agent in agentInfo.Keys)
             }
         }
     }
+    
+    public class BarracudaRecurrentInputGenerator : TensorGenerator.Generator
+    {
+        private bool firstHalf = true;
+        
+        public BarracudaRecurrentInputGenerator(bool firstHalf)
+        {
+            this.firstHalf = firstHalf;
+        }
+        
+        public void Generate(Tensor tensor, int batchSize, Dictionary<Agent, AgentInfo> agentInfo)
+        {
+            tensor.Shape[0] = batchSize;
+            var memorySize = tensor.Shape[tensor.Shape.Length - 1];
+            tensor.Data = new float[batchSize, memorySize];
+            var agentIndex = 0;
+            foreach (var agent in agentInfo.Keys)
+            {
+                var memory = agentInfo[agent].memories;
+
+                int offset = 0;
+                if (!firstHalf)
+                {
+                    offset = memory.Count - (int)memorySize;
+                }
+                
+                if (memory == null)
+                {
+                    agentIndex++;
+                    continue;
+                }
+                for (var j = 0; j < memorySize; j++)
+                {
+                    if (j >= memory.Count)
+                    {
+                        break;
+                    }
+                    tensor.Data.SetValue(memory[j + offset], new int[2] {agentIndex, j});
+                }
+                agentIndex++;
+            }
+        }
+    }
 
     /// <summary>
     /// Generates the Tensor corresponding to the Previous Action input : Will be a two
     {
         public void Generate(Tensor tensor, int batchSize, Dictionary<Agent, AgentInfo> agentInfo)
         {
-            if (tensor.ValueType != Tensor.TensorType.Integer)
-            {
-                throw new NotImplementedException(
-                    "Previous Action Inputs are only valid for discrete control");
-            }
-
-            var actionSize = tensor.Shape[1];
+            var actionSize = tensor.Shape[tensor.Shape.Length - 1];
             tensor.Data = new int[batchSize, actionSize];
             var agentIndex = 0;
             foreach (var agent in agentInfo.Keys)
         public void Generate(Tensor tensor, int batchSize, Dictionary<Agent, AgentInfo> agentInfo)
         {
             tensor.Shape[0] = batchSize;
-            var maskSize = tensor.Shape[1];
+            var maskSize = tensor.Shape[tensor.Shape.Length - 1];
             tensor.Data = new float[batchSize, maskSize];
             var agentIndex = 0;
             foreach (var agent in agentInfo.Keys)
         public void Generate(Tensor tensor, int batchSize, Dictionary<Agent, AgentInfo> agentInfo)
         {
             tensor.Shape[0] = batchSize;
-            var actionSize = tensor.Shape[1];
+            var actionSize = tensor.Shape[tensor.Shape.Length - 1];
             tensor.Data = new float[batchSize, actionSize];
             _randomNormal.FillTensor(tensor);
         }

UnitySDK/Assets/ML-Agents/Scripts/InferenceBrain/ModelParamLoader.cs

             Discrete,
             Continuous
         }
-        private const long ApiVersion = 1;
+        private const long ApiVersion = 2;
         private TFSharpInferenceEngine _engine;
         private BrainParameters _brainParameters;
         private List<string> _failedModelChecks = new List<string>();

UnitySDK/Assets/ML-Agents/Scripts/InferenceBrain/TensorApplier.cs

                     bp.vectorActionSize, seed);
             }
             _dict[TensorNames.RecurrentOutput] = new MemoryOutputApplier();
+            
+            _dict[TensorNames.RecurrentOutput_C] = new BarracudaMemoryOutputApplier(true);
+            _dict[TensorNames.RecurrentOutput_H] = new BarracudaMemoryOutputApplier(false);
         }
 
         /// <summary>

UnitySDK/Assets/ML-Agents/Scripts/InferenceBrain/TensorGenerator.cs

             _dict[TensorNames.SequenceLengthPlaceholder] = new SequenceLengthGenerator();
             _dict[TensorNames.VectorObservationPlacholder] = new VectorObservationGenerator();
             _dict[TensorNames.RecurrentInPlaceholder] = new RecurrentInputGenerator();
+            
+            _dict[TensorNames.RecurrentInPlaceholder_C] = new BarracudaRecurrentInputGenerator(true);
+            _dict[TensorNames.RecurrentInPlaceholder_H] = new BarracudaRecurrentInputGenerator(false);
+            
             _dict[TensorNames.PreviousActionPlaceholder] = new PreviousActionInputGenerator();
             _dict[TensorNames.ActionMaskPlaceholder] = new ActionMaskInputGenerator();
             _dict[TensorNames.RandomNormalEpsilonPlaceholder] = new RandomNormalInputGenerator(seed);

UnitySDK/Assets/ML-Agents/Scripts/InferenceBrain/TensorNames.cs

         public const string SequenceLengthPlaceholder = "sequence_length";
         public const string VectorObservationPlacholder = "vector_observation";
         public const string RecurrentInPlaceholder = "recurrent_in";
+        public const string RecurrentInPlaceholder_H = "recurrent_in_h";
+        public const string RecurrentInPlaceholder_C = "recurrent_in_c";
         public const string VisualObservationPlaceholderPrefix = "visual_observation_";
         public const string PreviousActionPlaceholder = "prev_action";
         public const string ActionMaskPlaceholder = "action_masks";
         public const string RecurrentOutput = "recurrent_out";
+        public const string RecurrentOutput_H = "recurrent_out_h";
+        public const string RecurrentOutput_C = "recurrent_out_c";
         public const string MemorySize = "memory_size";
         public const string VersionNumber = "version_number";
         public const string IsContinuousControl = "is_continuous_control";

UnitySDK/Assets/ML-Agents/Scripts/LearningBrain.cs

-using System;
+#define ENABLE_BARRACUDA
+
+using System;
+using Barracuda;
+using Tensor = MLAgents.InferenceBrain.Tensor;
+    public enum InferenceDevice
+    {
+        CPU = 0,
+        GPU = 1
+    }
+    
     /// <summary>
     /// The Learning Brain works differently if you are training it or not.
     /// When training your Agents, drag the Learning Brain to the Academy's BroadcastHub and check
         private TensorGenerator _tensorGenerator;
         private TensorApplier _tensorApplier;
 #if ENABLE_TENSORFLOW
-        private ModelParamLoader _modelParamLoader;
-#endif
-
-#if ENABLE_TENSORFLOW
+        private ModelParamLoader _modelParamLoader;
+#elif ENABLE_BARRACUDA 
+        public NNModel model;
+        private Model _barracudaModel;
+        private IWorker _engine;
+        private bool _verbose = false;
+        
+        private BarracudaModelParamLoader _modelParamLoader;
+        private string[] _outputNames;
+        [Tooltip("Inference execution device. CPU is the fastest option for most of ML Agents models. " +
+                 "(This field is not applicable for training).")]
+        public InferenceDevice inferenceDevice = InferenceDevice.CPU;
+        
         private IEnumerable<Tensor> _inferenceInputs;
         private IEnumerable<Tensor> _inferenceOutputs;
 
             _inferenceOutputs = _modelParamLoader.GetOutputTensors();
             _tensorGenerator = new TensorGenerator(brainParameters, seed);
             _tensorApplier = new TensorApplier(brainParameters, seed);
+#elif ENABLE_BARRACUDA
+            if (model != null)
+            {
+                #if BARRACUDA_VERBOSE
+                _verbose = true;
+                #endif
+
+                D.logEnabled = _verbose;
+                
+                // Cleanup previous instance
+                if (_engine != null)
+                    _engine.Dispose();
+                
+                _barracudaModel = ModelLoader.Load(model.Value);
+                var executionDevice = inferenceDevice == InferenceDevice.GPU
+                    ? BarracudaWorkerFactory.Type.ComputeFast
+                    : BarracudaWorkerFactory.Type.CSharpFast;
+                                       
+                _engine = BarracudaWorkerFactory.CreateWorker(executionDevice, _barracudaModel, _verbose);
+            }
+            else
+            {
+                _barracudaModel = null;
+                _engine = null;
+            }
+
+            _modelParamLoader = BarracudaModelParamLoader.GetLoaderAndCheck(_engine, _barracudaModel, brainParameters);
+            _inferenceInputs = _modelParamLoader.GetInputTensors();
+            _outputNames = _modelParamLoader.GetOutputNames();
+            _tensorGenerator = new TensorGenerator(brainParameters, seed);
+            _tensorApplier = new TensorApplier(brainParameters, seed);
 #endif
         }
         
         {
 
 #if ENABLE_TENSORFLOW
+            return (_modelParamLoader != null) ? _modelParamLoader.GetChecks() : new List<string>();
+#elif ENABLE_BARRACUDA
             return (_modelParamLoader != null) ? _modelParamLoader.GetChecks() : new List<string>();