diff --git a/AlphaGo/model.py b/AlphaGo/model.py
index c3bb9f0..6fde6e5 100644
--- a/AlphaGo/model.py
+++ b/AlphaGo/model.py
@@ -156,8 +156,6 @@ class ResNet(object):
         # Note : maybe we can use it for isolating test of MCTS
         #prob = [1.0 / self.action_num] * self.action_num
         #return [prob, np.random.uniform(-1, 1)]
-        if state is None:
-            return [[0.0] * self.action_num, 0]
         history, color = state
         if len(history) != self.history_length:
             raise ValueError(
diff --git a/tianshou/core/mcts/mcts.py b/tianshou/core/mcts/mcts.py
index 9625261..1251d05 100644
--- a/tianshou/core/mcts/mcts.py
+++ b/tianshou/core/mcts/mcts.py
@@ -112,11 +112,8 @@ class ActionNode(object):
             return self
 
     def expansion(self, prior, action_num):
-        if self.next_state is not None:
-            # note that self.next_state was assigned already at the selection function
-            # self.next_state is None means MCTS selected a terminate node
-            self.children[self.next_state_hashable] = UCTNode(self, self.action, self.next_state, action_num, prior,
-                                                     mcts=self.mcts, inverse=self.parent.inverse)
+        self.children[self.next_state_hashable] = UCTNode(self, self.action, self.next_state, action_num, prior,
+                                                          mcts=self.mcts, inverse=self.parent.inverse)
 
     def backpropagation(self, value):
         self.reward += value
@@ -183,8 +180,12 @@ class MCTS(object):
         t0 = time.time()
         next_action = self.root.selection(self.simulator)
         t1 = time.time()
-        prior, value = self.evaluator(next_action.next_state)
-        next_action.expansion(prior, self.action_num)
+        # next_action.next_state is None means the parent state node of next_action is a terminate node
+        if next_action.next_state is not None:
+            prior, value = self.evaluator(next_action.next_state)
+            next_action.expansion(prior, self.action_num)
+        else:
+            value = 0
         t2 = time.time()
         if self.inverse:
             next_action.backpropagation(-value + 0.)