diff --git a/brains/uttt.pth b/brains/uttt.pth
index 1c29941..919a499 100644
Binary files a/brains/uttt.pth and b/brains/uttt.pth differ
diff --git a/ultimatetictactoe.py b/ultimatetictactoe.py
index 3a6632f..8375db5 100644
--- a/ultimatetictactoe.py
+++ b/ultimatetictactoe.py
@@ -93,9 +93,14 @@ class TTTState(State):
         self.update_box_won()
         game_won = self.check_small_box(self.box_won)
         if game_won == '.':
+            if self.checkDraw():
+                return -1
             return None
         return game_won == 'X'
 
+    def checkDraw(self):
+        return len(self.getAvaibleActions())==0
+
     def __str__(self):
         state = self.board
         acts = list(self.getAvaibleActions())
diff --git a/vacuumDecay.py b/vacuumDecay.py
index 93f41bb..2b0e606 100644
--- a/vacuumDecay.py
+++ b/vacuumDecay.py
@@ -187,8 +187,6 @@ class Node():
 
     def _expand(self):
         self._childs = []
-        if self.getWinner()!=None:
-            return
         actions = self.state.getAvaibleActions()
         for action in actions:
             newNode = Node(self.state.mutate(action), self.universe, self, action)
@@ -287,12 +285,12 @@ class Node():
             self._calcScore(p)
 
     def _calcScore(self, player):
-        winner = self.getWinner()
+        winner = self._getWinner()
         if winner!=None:
-            if winner==-1:
-                self._scores[player] = 2/3 # draw
-            elif winner==player:
+            if winner==player:
                 self._scores[player] = 0.0
+            elif winner==-1:
+                self._scores[player] = 2/3
             else:
                 self._scores[player] = 1.0
             return
@@ -321,17 +319,20 @@ class Node():
     def curPlayer(self):
         return self.state.curPlayer
 
+    def _getWinner(self):
+        return self.state.checkWin()
+
     def getWinner(self):
         if len(self.childs)==0:
             return -1
-        return self.state.checkWin()
+        return self._getWinner()
 
     def _activateEdge(self, dist=0):
         if not self.strongScoresAvaible():
             self.universe.newOpen(self)
         else:
             for c in self.childs:
-                if c._cascadeMemory > 0.001*dist or random.random()<0.01:
+                if c._cascadeMemory > 0.001*(dist-2) or random.random()<0.01:
                     c._activateEdge(dist=dist+1)
 
     def __str__(self):
@@ -463,6 +464,7 @@ class NeuralRuntime(Runtime):
 class Trainer(Runtime):
     def __init__(self, initState):
         self.universe = Universe()
+        #self.universe = QueueingUniverse()
         self.rootNode = Node(initState, universe = self.universe)
         self.terminal = None
 
@@ -470,10 +472,12 @@ class Trainer(Runtime):
         print('[*] Building Timeline')
         term = self.linearPlay(model, calcDepth=depth, exacity=exacity)
         if refining:
-            print('[*] Refining Timeline')
+            print('[*] Refining Timeline (exploring alternative endings)')
             self.fanOut(term, depth=depth+2)
             self.fanOut(term.parent, depth=depth+2)
             self.fanOut(term.parent.parent, depth=depth+2)
+            #print('[*] Refining Timeline (exploring uncertain regions)')
+            #self.timelineExpandUncertain(term, 20)
         return term
 
     def fanOut(self, head, depth=4):
@@ -514,6 +518,15 @@ class Trainer(Runtime):
                 return
             head = head.parent
 
+    def timelineExpandUncertain(self, term, secs):
+        return
+        self.rootNode.universe.clearPQ()
+        self.rootNode.universe.activateEdge(rootNode)
+        self.spawnWorker()
+        time.sleep(secs)
+        self.rootNode.universe.clearPQ()
+        self.killWorker()
+
     def trainModel(self, model, lr=0.00005, cut=0.01, calcDepth=4, exacity=5, term=None):
         loss_func = nn.MSELoss()
         optimizer = optim.Adam(model.parameters(), lr)