Might be able to learn now (?)

bot.py

@@ -14,12 +14,13 @@ class Bot:
         with self.graph.as_default():
             self.session = tf.Session()
             self.network = Network(self.session)
+            self.network.restore_model()        
             
 
     def roll(self):
         print("{} rolled: ".format(self.sym))
         roll = self.cup.roll()
-        print(roll)
+#        print(roll)
         return roll
         
 

game.py

@@ -25,7 +25,7 @@ class Game:
 
     def next_round(self):
         roll = self.roll()
-        print(roll)
+        #print(roll)
         self.board = Board.flip(self.p2.make_move(Board.flip(self.board), self.p2.get_sym(), roll))
         return self.board
 
@@ -33,16 +33,22 @@ class Game:
         return self.board
 
     def train_model(self):
-        episodes = 100
+        episodes = 8000
         outcomes = []
         for episode in range(episodes):
             self.board = Board.initial_state
-            prev_board = self.board
+#            prev_board = self.board
+            prev_board, prev_board_value = self.roll_and_find_best_for_bot()
+            # find the best move here, make this move, then change turn as the
+            # first thing inside of the while loop and then call
+            # roll_and_find_best_for_bot to get V_t+1
+#            self.p1.make_move(prev_board, self.p1.get_sym(), self.roll())
             while Board.outcome(self.board) is None:
+                self.next_round()
                 cur_board, cur_board_value = self.roll_and_find_best_for_bot()
                 self.p1.get_network().train(prev_board, cur_board_value)
                 prev_board = cur_board
-                self.next_round()
+#                self.next_round()
                 # print("-"*30)
                 # print(Board.pretty(self.board))
                 # print("/"*30)
@@ -51,11 +57,13 @@ 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 % 10 == 0:
+            if episode % 100 == 0:
                 print("Saving...")
                 self.p1.get_network().save_model()
+                self.p2.restore_model()
 
         print(outcomes)
+        print(sum(outcomes))
 
     def next_round_test(self):
         print(self.board)

network.py

@@ -100,10 +100,10 @@ class Network:
             self.saver.restore(self.session, latest_checkpoint)
 
     # Have a circular dependency, #fuck, need to rewrite something
-    def train(self, x, v_next):
+    def train(self, board, v_next):
 #        print("lol")
-        x = np.array(x).reshape((1,26))
-        self.session.run(self.training_op, feed_dict = {self.x:x, self.value_next: v_next})
+        board = np.array(board).reshape((1,26))
+        self.session.run(self.training_op, feed_dict = {self.x:board, self.value_next: v_next})
                 
 
             # while game isn't done:

restore_bot.py

@@ -20,6 +20,10 @@ class RestoreBot:
     def get_sym(self):
         return self.sym
 
+    def restore_model(self):
+        with self.graph.as_default():
+            self.network.restore_model()
+
     def make_move(self, board, sym, roll):
         # print(Board.pretty(board))
         legal_moves = Board.calculate_legal_states(board, sym, roll)