Now only using one bot again. Also changed learning rate to 0.1

bot.py

@@ -27,6 +27,10 @@ class Bot:
     def switch(self,cur):
         return -1 if cur == 1 else 1
 
+    def restore_model(self):
+        with self.graph.as_default():
+            self.network.restore_model()
+            
     def get_session(self):
         return self.session
         

game.py

@@ -11,7 +11,7 @@ class Game:
         self.board = Board.initial_state
 
         self.p1 = Bot(1)
-        self.p2 = RestoreBot(1)
+        self.p2 = Bot(1)
         self.cup = Cup()
 
     def roll(self):
@@ -26,14 +26,14 @@ class Game:
     def next_round(self):
         roll = self.roll()
         #print(roll)
-        self.board = Board.flip(self.p2.make_move(Board.flip(self.board), self.p2.get_sym(), roll))
+        self.board = Board.flip(self.p2.make_move(Board.flip(self.board), self.p2.get_sym(), roll)[0])
         return self.board
 
     def board_state(self):
         return self.board
 
     def train_model(self):
-        episodes = 8000
+        episodes = 100
         outcomes = []
         for episode in range(episodes):
             self.board = Board.initial_state
@@ -57,10 +57,11 @@ class Game:
             final_score = np.array([ Board.outcome(self.board)[1] ]).reshape((1, 1))
             self.p1.get_network().train(prev_board, final_score)
             print("trained episode {}".format(episode))
-            if episode % 100 == 0:
+            if episode % 10 == 0:
                 print("Saving...")
                 self.p1.get_network().save_model()
                 self.p2.restore_model()
+                print(sum(outcomes))
 
         print(outcomes)
         print(sum(outcomes))
@@ -95,7 +96,7 @@ class Game:
             
             roll = self.roll()
             print("{} rolled: {}".format(self.p2.get_sym(), roll))
-            self.board = self.p2.make_move(self.board, self.p2.get_sym(), roll)
+            self.board = self.p2.make_move(self.board, self.p2.get_sym(), roll)[0]
 
 
         if Board.outcome(self.board)[1] > 0:

network.py

@@ -10,7 +10,7 @@ class Config():
     input_size = 26
     output_size = 1
     # Can't remember the best learning_rate, look this up
-    learning_rate = 0.3
+    learning_rate = 0.1
     checkpoint_path = "/tmp/"