name the saved model file

com.unity.ml-agents/Runtime/Training/MyTimeScaleSetting.cs

         s_Instance = null;
     }
 
+    [SerializeField]
+    string m_TrainingId = "default";
+    public string TrainingId
+    {
+        get { return m_TrainingId; }
+        set { m_TrainingId = value; }
+    }
+
 
     [SerializeField]
     float m_TimeScale = 1f;
     }
 
     [SerializeField]
-    bool m_LoadFile = true;
-    public bool LoadFile
+    bool m_LoadTrainedModel = true;
+    public bool LoadTrainedModel
-        get { return m_LoadFile; }
-        set { m_LoadFile = value; }
+        get { return m_LoadTrainedModel; }
+        set { m_LoadTrainedModel = value; }
+    }
+
+    [SerializeField]
+    Object m_Model;
+    public Object Model
+    {
+        get { return m_Model; }
+        set { m_Model = value; }
     }
 
     // Start is called before the first frame update

com.unity.ml-agents/Runtime/Training/TrainingModelRunner.cs

 
 using System.Collections.Generic;
 using Unity.Barracuda;
-using Unity.MLAgents;
-using Unity.MLAgents.Policies;
-using Unity.MLAgents.Inference.Utils;
-using System.Runtime.Serialization.Formatters.Binary;
+using UnityEditor;
 
 namespace Unity.MLAgents
 {
         bool m_TrainingObservationsInitialized;
 
         ReplayBuffer m_Buffer;
-        string m_ModelFileName = "Assets/model.dat";
 
         /// <summary>
         /// Initializes the Brain with the Model that it will use when selecting actions for
         {
             var initState = m_Model.GetTensorByName(TensorNames.InitialTrainingState);
             int[] stateShape = initState.shape.ToArray();
-            if (MyTimeScaleSetting.instance.LoadFile)
+            if (MyTimeScaleSetting.instance.LoadTrainedModel && MyTimeScaleSetting.instance.Model != null)
-                initState = LoadModelFromFile(stateShape);
+                var modelPath = AssetDatabase.GetAssetPath(MyTimeScaleSetting.instance.Model);
+                initState = LoadModelFromFile(modelPath, stateShape);
             }
 
             m_TrainingState = new TensorProxy
             float[] array = m_TrainingState.data.ToReadOnlyArray();
             var byteArray = new byte[array.Length * 4];
             Buffer.BlockCopy(array, 0, byteArray, 0, byteArray.Length);
-            File.WriteAllBytes(m_ModelFileName, byteArray);
-            Debug.Log($"Save ModelParam: {m_TrainingState.data[0]}, {m_TrainingState.data[1]}, {m_TrainingState.data[2]}, " +
-                $"{m_TrainingState.data[3]}, {m_TrainingState.data[4]}, {m_TrainingState.data[5]}, " +
-                $"{m_TrainingState.data[6]}, {m_TrainingState.data[7]}, {m_TrainingState.data[8]}, {m_TrainingState.data[9]}");
+            File.WriteAllBytes("Assets/model_" + MyTimeScaleSetting.instance.TrainingId, byteArray);
-        public Tensor LoadModelFromFile(int[] shape)
+        public Tensor LoadModelFromFile(string path, int[] shape)
-            var byteArray = File.ReadAllBytes(m_ModelFileName);
+            var byteArray = File.ReadAllBytes(path);
             float[] array = new float[byteArray.Length / 4];
             Buffer.BlockCopy(byteArray, 0, array, 0, byteArray.Length);
             return new Tensor(shape, array);