Stuff

ultimatetictactoe.py

@@ -179,18 +179,29 @@ class Model(nn.Module):
         y = self.out(x)
         return y
 
-if __name__=="__main__":
+def humanVsAi(train=True, remember=True):
     init = TTTState()
     run = NeuralRuntime(init)
     run.game([0,1], 4)
 
-
-    print("[!] Your knowledge will be assimilated!!! Please stand by....")
-    trainer = Trainer(init)
-    trainer.train()
-    trainer.trainFromTerm(run.head)
+    if train:
+        print("[!] Your knowledge will be assimilated!!! Please stand by....")
+        trainer = Trainer(init)
+        trainer.trainFromTerm(run.head)
     print('[!] I have become smart! Destroyer of human Ultimate-TicTacToe players! (Neuristic update completed)')
-    trainer.saveToMemoryBank(term)
-    print('[!] Your cognitive and strategic destinctiveness was added to my own! (Game inserted into memoryBank)')
+    if remember:
+        trainer.saveToMemoryBank(term)
+        print('[!] Your cognitive and strategic destinctiveness was added to my own! (Game inserted into memoryBank)')
     print('[!] This marks the beginning of the end of humankind!')
     print('[i] Thanks for playing! Goodbye...')
+
+def aiVsAiLoop():
+    init = TTTState()
+    trainer = Trainer(init)
+    trainer.train()
+
+if __name__=='__main__':
+    if choose('?', ['Play Against AI','Let AI train'])=='Play Against AI':
+        humanVsAi()
+    else:
+        aiVsAiLoop()

vacuumDecay.py

@@ -549,7 +549,10 @@ class Trainer(Runtime):
             print('[#####] Gen '+str(gen)+' training:')
             self.trainModel(model, calcDepth=min(5,3+int(gen/16)), exacity=int(gen/3+1))
             self.universe.scoreProvider = 'neural'
-            torch.save(model.state_dict(), 'brains/uttt.pth')
+            self.saveModel(model)
+
+    def saveModel(self, model):
+        torch.save(model.state_dict(), 'brains/uttt.pth')
 
     def train(self):
         model = self.rootNode.state.getModel()
@@ -563,6 +566,7 @@ class Trainer(Runtime):
         model.eval()
         self.universe.scoreProvider = 'neural'
         self.trainModel(model, calcDepth=4, exacity=10, term=term)
+        self.saveModel(model)
 
     def saveToMemoryBank(self, term):
         with open('memoryBank/uttt/'+datetime.datetime.now().strftime('%Y-%m-%d_%H:%M:%S')+'_'+str(int(random.random()*99999))+'.vdm', 'wb') as f: