diff --git a/ultimatetictactoe.py b/ultimatetictactoe.py
index 1ddad80..025b539 100644
--- a/ultimatetictactoe.py
+++ b/ultimatetictactoe.py
@@ -86,8 +86,8 @@ class TTTState(State):
     #            sco -= 0.5
     #    return 1/sco
 
-    def getPriority(self, score, cascadeMem):
-        return self.generation + score*10 - cascadeMem*5 + 100
+    #def getPriority(self, score, cascadeMem):
+    #    return -cascadeMem*1 + 100
 
     def checkWin(self):
         self.update_box_won()
@@ -151,45 +151,47 @@ class Model(nn.Module):
         self.smol = nn.Sequential(
             nn.Conv2d(
                 in_channels=1,
-                out_channels=24,
+                out_channels=16,
                 kernel_size=(3,3),
                 stride=3,
                 padding=0,
             ),
             nn.ReLU()
         )
-        self.comb = nn.Sequential(
-            nn.Conv1d(
-                in_channels=24,
-                out_channels=8,
-                kernel_size=1,
-                stride=1,
-                padding=0,
-            ),
-            nn.ReLU()
-        )
+        #self.comb = nn.Sequential(
+        #    nn.Conv1d(
+        #        in_channels=24,
+        #        out_channels=8,
+        #        kernel_size=1,
+        #        stride=1,
+        #        padding=0,
+        #    ),
+        #    nn.ReLU()
+        #)
         self.out = nn.Sequential(
-            nn.Linear(9*8, 32),
+            #nn.Linear(9*8, 32),
+            #nn.ReLU(),
+            #nn.Linear(32, 8),
+            #nn.ReLU(),
+            nn.Linear(16*9, 12),
             nn.ReLU(),
-            nn.Linear(32, 8),
-            nn.ReLU(),
-            nn.Linear(8, 1),
+            nn.Linear(12, 1),
             nn.Sigmoid()
         )
 
     def forward(self, x):
         x = torch.reshape(x, (1,9,9))
         x = self.smol(x)
-        x = torch.reshape(x, (24,9))
-        x = self.comb(x)
+        #x = torch.reshape(x, (24,9))
+        #x = self.comb(x)
         x = torch.reshape(x, (-1,))
         y = self.out(x)
         return y
 
-def humanVsAi(train=True, remember=False):
+def humanVsAi(train=True, remember=False, depth=3, bots=[0,1], noBg=False):
     init = TTTState()
     run = NeuralRuntime(init)
-    run.game([0,1], 3)
+    run.game(bots, depth, bg=not noBg)
 
     if remember or train:
         trainer = Trainer(init)
@@ -209,7 +211,15 @@ def aiVsAiLoop():
     trainer.train()
 
 if __name__=='__main__':
-    if choose('?', ['Play Against AI','Let AI train'])=='Play Against AI':
+    options = ['Play Against AI','Play Against AI (AI begins)','Play Against AI (Fast Play)','Playground','Let AI train']
+    opt = choose('?', options)
+    if opt == options[0]:
         humanVsAi()
+    elif opt == options[1]:
+        humanVsAi(bots[1,0])
+    elif opt == options[2]:
+        humanVsAi(depth=2,noBg=True)
+    elif opt == options[3]:
+        humanVsAi(bots=[None,None])
     else:
         aiVsAiLoop()