diff --git a/unity-environment/Assets/ML-Agents/Scripts/CoreBrainInternal.cs b/unity-environment/Assets/ML-Agents/Scripts/CoreBrainInternal.cs
index 6fd62ceb3f..8362284f69 100755
--- a/unity-environment/Assets/ML-Agents/Scripts/CoreBrainInternal.cs
+++ b/unity-environment/Assets/ML-Agents/Scripts/CoreBrainInternal.cs
@@ -51,7 +51,7 @@ public enum tensorType
     public string[] ObservationPlaceholderName;
     /// Modify only in inspector : Name of the action node
     public string ActionPlaceholderName = "action";
-#if ENABLE_TENSORFLOW
+    #if ENABLE_TENSORFLOW
     TFGraph graph;
     TFSession session;
     bool hasRecurrent;
@@ -62,7 +62,7 @@ public enum tensorType
     float[,] inputState;
     List<float[,,,]> observationMatrixList;
     float[,] inputOldMemories;
-#endif
+    #endif
 
     /// Reference to the brain that uses this CoreBrainInternal
     public Brain brain;
@@ -190,13 +190,22 @@ public void DecideAction()
 
         foreach (TensorFlowAgentPlaceholder placeholder in graphPlaceholders)
         {
-            if (placeholder.valueType == TensorFlowAgentPlaceholder.tensorType.FloatingPoint)
+            try
             {
-                runner.AddInput(graph[graphScope + placeholder.name][0], new float[] { Random.Range(placeholder.minValue, placeholder.maxValue) });
+                if (placeholder.valueType == TensorFlowAgentPlaceholder.tensorType.FloatingPoint)
+                {
+                    runner.AddInput(graph[graphScope + placeholder.name][0], new float[] { Random.Range(placeholder.minValue, placeholder.maxValue) });
+                }
+                else if (placeholder.valueType == TensorFlowAgentPlaceholder.tensorType.Integer)
+                {
+                    runner.AddInput(graph[graphScope + placeholder.name][0], new int[] { Random.Range((int)placeholder.minValue, (int)placeholder.maxValue + 1) });
+                }
             }
-            else if (placeholder.valueType == TensorFlowAgentPlaceholder.tensorType.Integer)
+            catch
             {
-                runner.AddInput(graph[graphScope + placeholder.name][0], new int[] { Random.Range((int)placeholder.minValue, (int)placeholder.maxValue + 1) });
+                throw new UnityAgentsException(string.Format(@"One of the Tensorflow placeholder cound nout be found.
+                In brain {0}, there are no {1} placeholder named {2}.",
+                        brain.gameObject.name, placeholder.valueType.ToString(), graphScope + placeholder.name));
             }
         }
 
@@ -212,6 +221,26 @@ public void DecideAction()
             runner.AddInput(graph[graphScope + ObservationPlaceholderName[obs_number]][0], observationMatrixList[obs_number]);
         }
 
+        TFTensor[] networkOutput;
+        try
+        {
+            networkOutput = runner.Run();
+        }
+        catch (TFException e)
+        {
+            string errorMessage = e.Message;
+            try
+            {
+                errorMessage = string.Format(@"The tensorflow graph needs an input for {0} of type {1}",
+                    e.Message.Split(new string[]{ "Node: " }, 0)[1].Split('=')[0],
+                    e.Message.Split(new string[]{ "dtype=" }, 0)[1].Split(',')[0]);
+            }
+            finally
+            {
+                throw new UnityAgentsException(errorMessage);
+            }
+
+        }
 
         // Create the recurrent tensor
         if (hasRecurrent)
@@ -220,7 +249,7 @@ public void DecideAction()
 
             runner.AddInput(graph[graphScope + RecurrentInPlaceholderName][0], inputOldMemories);
             runner.Fetch(graph[graphScope + RecurrentOutPlaceholderName][0]);
-            float[,] recurrent_tensor = runner.Run()[1].GetValue() as float[,];
+            float[,] recurrent_tensor = networkOutput[1].GetValue() as float[,];
 
             int i = 0;
             foreach (int k in agentKeys)
@@ -241,7 +270,7 @@ public void DecideAction()
 
         if (brain.brainParameters.actionSpaceType == StateType.continuous)
         {
-            float[,] output = runner.Run()[0].GetValue() as float[,];
+            float[,] output = networkOutput[0].GetValue() as float[,];
             int i = 0;
             foreach (int k in agentKeys)
             {
@@ -256,7 +285,7 @@ public void DecideAction()
         }
         else if (brain.brainParameters.actionSpaceType == StateType.discrete)
         {
-            long[,] output = runner.Run()[0].GetValue() as long[,];
+            long[,] output = networkOutput[0].GetValue() as long[,];
             int i = 0;
             foreach (int k in agentKeys)
             {