Update Barracuda to 0.5.0-preview. (#3329)

Project/Assets/ML-Agents/Examples/3DBall/TFModels/3DBall.nn.meta

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Project/Assets/ML-Agents/Examples/3DBall/TFModels/3DBallHard.nn.meta

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Project/Assets/ML-Agents/Examples/Basic/TFModels/Basic.nn.meta

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Project/Assets/ML-Agents/Examples/Bouncer/TFModels/Bouncer.nn.meta

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   fileIDToRecycleName:
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Project/Assets/ML-Agents/Examples/Crawler/TFModels/CrawlerDynamic.nn.meta

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Project/Assets/ML-Agents/Examples/Crawler/TFModels/CrawlerStatic.nn.meta

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Project/Assets/ML-Agents/Examples/FoodCollector/TFModels/FoodCollector.nn.meta

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   fileIDToRecycleName:
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Project/Assets/ML-Agents/Examples/GridWorld/TFModels/GridWorld.nn.meta

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Project/Assets/ML-Agents/Examples/Hallway/TFModels/Hallway.nn.meta

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Project/Assets/ML-Agents/Examples/PushBlock/TFModels/PushBlock.nn.meta

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   fileIDToRecycleName:
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Project/Assets/ML-Agents/Examples/Pyramids/TFModels/Pyramids.nn.meta

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   fileIDToRecycleName:
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Project/Assets/ML-Agents/Examples/Reacher/TFModels/Reacher.nn.meta

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

             }
 
             var asset = ScriptableObject.CreateInstance<NNModel>();
-            asset.Value = model;
+            asset.modelData.Value = model;
             asset.name = "Override - " + Path.GetFileName(assetPath);
             m_CachedModels[behaviorName] = asset;
             return asset;

Project/Assets/ML-Agents/Examples/Tennis/TFModels/Tennis.nn.meta

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   fileIDToRecycleName:
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+    11400002: model data
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Project/Assets/ML-Agents/Examples/Walker/TFModels/Walker.nn.meta

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Project/Assets/ML-Agents/Examples/WallJump/TFModels/BigWallJump.nn.meta

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Project/Assets/ML-Agents/Examples/WallJump/TFModels/SmallWallJump.nn.meta

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

             var brainParameters = behaviorParameters.brainParameters;
             if (model != null)
             {
-                barracudaModel = ModelLoader.Load(model.Value);
+                barracudaModel = ModelLoader.Load(model);
             }
             if (brainParameters != null)
             {

com.unity.ml-agents/Runtime/InferenceBrain/ModelRunner.cs

 
                 D.logEnabled = m_Verbose;
 
-                barracudaModel = ModelLoader.Load(model.Value);
+                barracudaModel = ModelLoader.Load(model);
-                    ? BarracudaWorkerFactory.Type.ComputePrecompiled
-                    : BarracudaWorkerFactory.Type.CSharp;
-                m_Engine = BarracudaWorkerFactory.CreateWorker(executionDevice, barracudaModel, m_Verbose);
+                    ? WorkerFactory.Type.ComputePrecompiled
+                    : WorkerFactory.Type.CSharp;
+                m_Engine = WorkerFactory.CreateWorker(executionDevice, barracudaModel, m_Verbose);
             }
             else
             {
             var outputs = new List<TensorProxy>();
             foreach (var n in names)
             {
-                var output = m_Engine.Peek(n);
+                var output = m_Engine.PeekOutput(n);
                 outputs.Add(TensorUtils.TensorProxyFromBarracuda(output, n));
             }
 

com.unity.ml-agents/package.json

 	"displayName":"ML Agents",
 	"version": "0.13.0-preview",
 	"unity": "2018.4",
-	"description": "Add interactivity to your game with ML-Agents trained using Deep Reinforcement Learning. \n\nFor best results, use this text to summarize: \n\u25AA What the package does \n\u25AA How it can benefit the user \n\nNote: Special formatting characters are supported, including line breaks ('\\n') and bullets ('\\u25AA').",
+	"description": "Add interactivity to your game with ML-Agents trained using Deep Reinforcement Learning.",
-		"com.unity.barracuda": "0.4.0-preview"
+		"com.unity.barracuda": "0.5.0-preview"
 	}
 }