Minor changes

qlearning.py

@@ -27,15 +27,16 @@ class QTable:
     ##### Reward +1 when eat an apple
     ##### Reward -10 when hit obstacle
     """
-    def __init__(self, file, save_every=10):
+    def __init__(self, file, save_every=5000):
         self.file=file
         self.save_every=save_every
-        self.update_counter=0
+        self.save_counter=0
         if os.path.exists(file):
             self.qtable=np.loadtxt(file)
         else:
             self.qtable=np.zeros((2**13, 4))
-
+            with open(file+"_generation","w") as f:
+                f.write("0")
 
     def isWall(self,h,game):
         if h[0]<0 or h[1]<0 or h[0] >= game.grid_width or h[1] >= game.grid_height:
@@ -108,10 +109,18 @@ class QTable:
         alpha=0.5
         gamma=0.9
         self.qtable[state,action]=self.qtable[state,action]+alpha*(reward+gamma*np.max(self.qtable[new_state])-self.qtable[state,action])
-        self.update_counter+=1
+        self.save_counter+=1
-        if self.update_counter>=self.save_every:
+        if self.save_counter>=self.save_every:
             np.savetxt(self.file,self.qtable)
-            self.update_counter=0
+            if os.path.exists(self.file+"_generation"):
+                generation=0
+                with open(self.file+"_generation","r") as f:
+                    generation=int(f.readline().rstrip())
+                generation+=self.save_every
+                with open(self.file+"_generation","w") as f:
+                    f.write(str(generation))
+                print("Checkpointing generation "+str(generation))
+            self.save_counter=0
 
     def get_action(self,state):
         # Choose an action
@@ -123,12 +132,15 @@ class QTable:
             action=np.argmax(self.qtable[state])
         return(action)
     
+    def get_random_action(self):
+        return(random.choice((0,1,2,3)))
+
 
 
 
 
 # Perform learning
-width,height=10,10
+width,height=50,30
 perf=0
 last_state=None
 last_action=None
@@ -138,25 +150,34 @@ while True:
     result=0
     stuck=0
     stuck_tolerance=1
+    stuck_count=0
     state=qtable.get_state(game)
     while result >= 0: 
         action=qtable.get_action(state)
         result=game.play3(action)
         new_state=qtable.get_state(game)
 
-        # Agent is stuck
-        if stuck>=(game.grid_width*game.grid_height)/stuck_tolerance:
-            game.new_game()
-            break
-
         # Compute reward and update stuck
         reward=0
         if result==-1:
             reward=-10
             stuck=0
+            stuck_count=0
         elif result==1:
-            reward=50
+            reward=1
             stuck=0
+            stuck_count=0
+
+        # Agent is stuck
+        if stuck>=(game.grid_width*game.grid_height)/stuck_tolerance:
+            stuck=0
+            stuck_count+=1
+            action=qtable.get_random_action()
+            print("Stuck!")
+        if stuck_count>2:
+            stuck_count=0
+            game.new_game()
+            break
 
         # Apply learning    
         qtable.apply_bellman(state,action,new_state,reward)

snake.py

@@ -216,3 +216,7 @@ class Snake:
             if self.play(self.direction,handle_quit=False) <0:
                 break
             
+
+if __name__ == "__main__":
+    game=Snake(length=50)
+    game.play_with_keyboard()