Yet another input argument, "--ply", 0 for no look-ahead, 1 for a single

look-ahead.

main.py

@@ -40,6 +40,8 @@ parser.add_argument('--use-baseline', action='store_true',
                     help='use the baseline model, note, has size 28')
 parser.add_argument('--verbose', action='store_true',
                     help='If set, a lot of stuff will be printed')
+parser.add_argument('--ply', action='store', dest='ply',
+                    help='defines the amount of ply used when deciding what move to make')
 
 args = parser.parse_args()
 
@@ -64,7 +66,8 @@ config = {
     'force_creation': args.force_creation,
     'use_baseline': args.use_baseline,
     'global_step': 0,
-    'verbose': args.verbose
+    'verbose': args.verbose,
+    'ply': args.ply
 
 }
 

network.py

@@ -23,6 +23,7 @@ class Network:
         'tesauro-poop': (198, Board.board_features_tesauro_wrong)
     }
 
+
     def custom_tanh(self, x, name=None):
         return tf.scalar_mul(tf.constant(2.00), tf.tanh(x, name))
 
@@ -31,6 +32,12 @@ class Network:
         :param config:
         :param name:
         """
+
+        move_options = {
+            '1': self.make_move_1_ply,
+            '0': self.make_move_0_ply
+        }
+
         tf.enable_eager_execution()
 
         xavier_init = tf.contrib.layers.xavier_initializer()
@@ -40,6 +47,10 @@ class Network:
 
         self.name = name
 
+        self.make_move = move_options[
+            self.config['ply']
+        ]
+
         # Set board representation from config
         self.input_size, self.board_trans_func = Network.board_reps[
             self.config['board_representation']
@@ -191,7 +202,7 @@ class Network:
 
 
 
-    def make_move(self, board, roll, player):
+    def make_move_0_ply(self, board, roll, player):
         """
         Find the best move given a board, roll and a player, by finding all possible states one can go to
         and then picking the best, by using the network to evaluate each state. The highest score is picked
@@ -218,17 +229,16 @@ class Network:
 
         return [best_move, best_score]
 
-    def make_move_n_ply(self, sess, board, roll, player, n = 1):
+    def make_move_1_ply(self, board, roll, player):
         """
-
-        :param sess:
         :param board:
         :param roll:
         :param player:
-        :param n:
         :return:
         """
-        best_pair = self.calc_n_ply(n, sess, board, player, roll)
+        start = time.time()
+        best_pair = self.calculate_1_ply(board, roll, player)
+        print(time.time() - start)
         return best_pair
 
 
@@ -303,7 +313,7 @@ class Network:
 
         all_rolls = gen_21_rolls()
 
-        start = time.time()
+        # start = time.time()
 
         list_of_moves = []
 
@@ -318,9 +328,8 @@ class Network:
             list_of_moves.append(np.array(all_board_moves))
 
 
-        print(time.time() - start)
-
-        start = time.time()
+        # print(time.time() - start)
+        # start = time.time()
 
         # Running data through networks
         all_scores = [self.model.predict_on_batch(board) for board in list_of_moves]
@@ -328,7 +337,7 @@ class Network:
 
         transformed_means = [x if player == 1 else (1-x) for x in scores_means]
 
-        print(time.time() - start)
+        # print(time.time() - start)
         return ([scores_means, transformed_means])