Added code for 2-ply look-ahead

network.py

@@ -8,6 +8,7 @@ import sys
 import random
 from eval import Eval
 import glob
+from operator import itemgetter
 
 class Network:
     # board_features_quack has size 28
@@ -35,13 +36,16 @@ class Network:
         ]
         self.output_size = 1
         self.hidden_size = 40
-        # Can't remember the best learning_rate, look this up
         self.max_learning_rate = 0.1
         self.min_learning_rate = 0.001
-        # self.learning_rate = 0.01
 
         self.global_step = tf.Variable(0, trainable=False, name="global_step")
-        self.learning_rate = tf.maximum(self.min_learning_rate, tf.train.exponential_decay(self.max_learning_rate, self.global_step, 50000, 0.96, staircase=True), name="learning_rate")
+        self.learning_rate = tf.maximum(self.min_learning_rate,
+                                        tf.train.exponential_decay(self.max_learning_rate,
+                                                                   self.global_step, 50000,
+                                                                   0.96,
+                                                                   staircase=True),
+                                        name="learning_rate")
 
         
         
@@ -53,7 +57,6 @@ class Network:
         else:
             self.episodes_trained = 0
 
-        # input = x
         self.x = tf.placeholder('float', [1, self.input_size], name='input')
         self.value_next = tf.placeholder('float', [1, self.output_size], name="value_next")
 
@@ -105,40 +108,6 @@ class Network:
         self.saver = tf.train.Saver(max_to_keep=1)
 
     def eval_state(self, sess, state):
-        # Run state through a network
-
-        # Remember to create placeholders for everything because wtf tensorflow
-        # and graphs
-
-        # Remember to create the dense layers
-
-        # Figure out a way of giving a layer a custom activiation function (we
-        # want something which gives [-2,2]. Naively tahn*2, however I fell this
-        # is wrong.
-
-        # tf.group, groups a bunch of actions, so calculate the different
-        # gradients for the different weights, by using tf.trainable_variables()
-        # to find all variables and tf.gradients(current_value,
-        # trainable_variables) to find all the gradients. We can then loop
-        # through this and calculate the trace for each gradient and variable
-        # pair (note, zip can be used to combine the two lists found before),
-        # and then we can calculate the overall change in weights, based on the
-        # formula listed in tesauro (learning_rate * difference_in_values *
-        # trace), this calculation can be assigned to a tf variable and put in a
-        # list and then this can be grouped into a single operation, essentially
-        # building our own backprop function.
-
-        # Grouping them is done by
-        # tf.group(*the_gradients_from_before_we_want_to_apply,
-        # name="training_op")
-
-        # If we remove the eligibily trace to begin with, we only have to
-        # implement learning_rate * (difference_in_values) * gradients (the
-        # before-mentioned calculation.
-
-        # print("Network is evaluating")
-        # print("eval ({})".format(self.name), state, val, sep="\n")
-
         return sess.run(self.value, feed_dict={self.x: state})
 
     def save_model(self, sess, episode_count, global_step):
@@ -170,16 +139,69 @@ class Network:
 
 
     def make_move(self, sess, board, roll, player):
-        # print(Board.pretty(board))
         legal_moves = Board.calculate_legal_states(board, player, roll)
         moves_and_scores = [(move, self.eval_state(sess, self.board_trans_func(move, player))) for move in legal_moves]
         scores = [x[1] if np.sign(player) > 0 else 1-x[1] for x in moves_and_scores]
         best_score_index = np.array(scores).argmax()
         best_move_pair = moves_and_scores[best_score_index]
-        # print("Found the best state, being:", np.array(move_scores).argmax())
         return best_move_pair
 
+
+    def gen_21_rolls(self):
+        a = []
+        for x in range(1,7):
+            for y in range(1,7):
+                if not [x,y] in a and not [y,x] in a:
+                    a.append([x,y])
+
+        return a
+
+    def calculate_2_ply(self, sess, board, roll, player):
+        """
+        Find the best move based on a 2-ply look-ahead. First the best move is found for a single ply and then an
+        exhaustive search is performed on the best 15 moves from the single ply.
+
+        :param sess:
+        :param board:
+        :param roll: The original roll
+        :param player: The current player
+        :return: Best possible move based on 2-ply look-ahead
+
+        """
+
+
+        init_legal_states = Board.calculate_legal_states(board, player, roll)
+        zero_ply_moves_and_scores = [(move, self.eval_state(sess, self.board_trans_func(move, player))) for move in init_legal_states]
+
+        # pythons reverse is in place and I can't call [:15] on it, without applying it to an object like so. Fuck.
+        best_fifteen = sorted(zero_ply_moves_and_scores, key=itemgetter(1))
+        best_fifteen.reverse()
+        best_fifteen_boards = [x[0] for x in best_fifteen[:15]]
+
+        all_rolls = self.gen_21_rolls()
+
+        all_rolls_scores = []
+        for a_board in best_fifteen_boards:
+            a_board_scores = []
+            for roll in all_rolls:
+                spec_roll_scores = []
+                all_rolls_boards = Board.calculate_legal_states(a_board, player*-1, roll)
+
+                spec_roll_scores.append([self.eval_state(sess, self.board_trans_func(new_board, player*-1)) for new_board in all_rolls_boards])
+
+                best_score = max(spec_roll_scores)
+
+                a_board_scores.append(best_score)
+
+            all_rolls_scores.append(sum(a_board_scores)/len(a_board_scores))
+
+        best_score_index = np.array(all_rolls_scores).argmax()
+        best_board = best_fifteen_boards[best_score_index]
+
+        return [best_board, max(all_rolls_scores)]
+
     def eval(self, episode_count, trained_eps = 0, tf_session = None):
+
         def do_eval(sess, method, episodes = 1000, trained_eps = 0):
             start_time = time.time()
 
@@ -198,47 +220,20 @@ class Network:
             sys.stderr.write(
                 "[EVAL ] Evaluating {eps} episode(s) with method '{method}'\n".format(eps=episodes, method=method))
 
-            if method == 'random':
-                outcomes = []
-                """for i in range(1, episodes + 1):
-                    sys.stderr.write("[EVAL ] Episode {}".format(i))
-                    board = Board.initial_state
-                    while Board.outcome(board) is None:
-                        roll = (random.randrange(1, 7), random.randrange(1, 7))
-                        board = (self.p1.make_move(sess, board, self.p1.get_sym(), roll))[0]
-                        roll = (random.randrange(1, 7), random.randrange(1, 7))
-                        board = Board.flip(Eval.make_random_move(Board.flip(board), 1, roll))
-                    sys.stderr.write("\t outcome {}".format(Board.outcome(board)[1]))
-                    outcomes.append(Board.outcome(board)[1])
-                    sys.stderr.write("\n")
 
-                    if i % 50 == 0:
+            if method == 'pubeval':
-                        print_time_estimate(i)"""
-                return outcomes
-            elif method == 'pubeval':
                 outcomes = []
-                # Add the evaluation code for pubeval, the bot has a method make_pubeval_move(board, sym, roll),
-                #  which can be used to get the best move according to pubeval
                 for i in range(1, episodes + 1):
                     sys.stderr.write("[EVAL ] Episode {}".format(i))
                     board = Board.initial_state
-                    # print("init:", board, sep="\n")
                     while Board.outcome(board) is None:
-                        # print("-"*30)
                         roll = (random.randrange(1, 7), random.randrange(1, 7))
-                        # print(roll)
 
-                        # prev_board = tuple(board)
                         board = (self.make_move(sess, board, roll, 1))[0]
-                        # print("post p1:", board, sep="\n")
 
-                        # print("."*30)
                         roll = (random.randrange(1, 7), random.randrange(1, 7))
-                        # print(roll)
 
-                        # prev_board = tuple(board)
                         board = Eval.make_pubeval_move(board, -1, roll)[0][0:26]
-                        # print("post pubeval:", board, sep="\n")
 
                     sys.stderr.write("\t outcome {}".format(Board.outcome(board)[1]))
                     outcomes.append(Board.outcome(board)[1])
@@ -251,28 +246,17 @@ class Network:
 
             elif method == 'dumbeval':
                 outcomes = []
-                # Add the evaluation code for pubeval, the bot has a method make_pubeval_move(board, sym, roll),
-                #  which can be used to get the best move according to pubeval
                 for i in range(1, episodes + 1):
                     sys.stderr.write("[EVAL ] Episode {}".format(i))
                     board = Board.initial_state
-                    # print("init:", board, sep="\n")
                     while Board.outcome(board) is None:
-                        # print("-"*30)
                         roll = (random.randrange(1, 7), random.randrange(1, 7))
-                        # print(roll)
 
-                        # prev_board = tuple(board)
                         board = (self.make_move(sess, board, roll, 1))[0]
-                        # print("post p1:", board, sep="\n")
 
-                        # print("."*30)
                         roll = (random.randrange(1, 7), random.randrange(1, 7))
-                        # print(roll)
 
-                        # prev_board = tuple(board)
                         board = Eval.make_dumbeval_move(board, -1, roll)[0][0:26]
-                        # print("post pubeval:", board, sep="\n")
 
                     sys.stderr.write("\t outcome {}".format(Board.outcome(board)[1]))
                     outcomes.append(Board.outcome(board)[1])
@@ -283,31 +267,6 @@ class Network:
 
                 return outcomes
 
-            elif method == 'dumbmodel':
-                outcomes = []
-                """
-                config_prime = self.config.copy()
-                config_prime['model_path'] = os.path.join(config_prime['model_storage_path'], 'dumbmodel')
-                eval_bot = Bot(1, config = config_prime, name = "dumbmodel")
-                #print(self.config, "\n", config_prime)
-                outcomes = []
-                for i in range(1, episodes + 1):
-                sys.stderr.write("[EVAL ] Episode {}".format(i))
-                board = Board.initial_state
-                while Board.outcome(board) is None:
-                roll = (random.randrange(1,7), random.randrange(1,7))
-                board = (self.make_move(board, self.p1.get_sym(), roll))[0]
-
-                roll = (random.randrange(1,7), random.randrange(1,7))
-                board = Board.flip(eval_bot.make_move(Board.flip(board), self.p1.get_sym(), roll)[0])
-                sys.stderr.write("\t outcome {}".format(Board.outcome(board)[1]))
-                outcomes.append(Board.outcome(board)[1])
-                sys.stderr.write("\n")
-
-                if i % 50 == 0:
-                print_time_estimate(i)
-                """
-                return outcomes
             else:
                 sys.stderr.write("[EVAL ] Evaluation method '{}' is not defined\n".format(method))
                 return [0]
@@ -363,27 +322,20 @@ class Network:
             sys.stderr.write("[TRAIN] Training {} episodes and save_step_size {}\n".format(episodes, save_step_size))
             outcomes = []
             for episode in range(1, episodes + 1):
+
                 sys.stderr.write("[TRAIN] Episode {}".format(episode + trained_eps))
                 # TODO decide which player should be here
 
                 player = 1
-
                 prev_board = Board.initial_state
-
-                # find the best move here, make this move, then change turn as the
-                # first thing inside of the while loop and then call
-                # best_move_and_score to get V_t+1
-
                 i = 0
                 while Board.outcome(prev_board) is None:
                     i += 1
 
-                    #print("PREEEV_BOOOOAAARD:",prev_board)
                     cur_board, cur_board_value = self.make_move(sess,
                                                                 prev_board,
-                                                                (random.randrange(1, 7), random.randrange(1, 7)), player)
+                                                                (random.randrange(1, 7), random.randrange(1, 7)),
-
+                                                                player)
-                    #print("The current value:",cur_board_value)
 
                     # adjust weights
                     sess.run(self.training_op,
@@ -392,7 +344,6 @@ class Network:
 
                     player *= -1
 
-
                     prev_board = cur_board
 
                 final_board = prev_board
@@ -400,8 +351,6 @@ class Network:
                 outcomes.append(Board.outcome(final_board)[1])
                 final_score = np.array([Board.outcome(final_board)[1]])
                 scaled_final_score = ((final_score + 2) / 4)
-                #print("The difference in values:", scaled_final_score - cur_board_value)
-                # print("scaled_final_score",scaled_final_score)
 
                 with tf.name_scope("final"):
                     merged = tf.summary.merge_all()
@@ -425,11 +374,3 @@ class Network:
             writer.close()
         
             return outcomes
-
-            
-                # take turn, which finds the best state and picks it, based on the current network
-                # save current state
-                # run training operation (session.run(self.training_op, {x:x, value_next, value_next})), (something which does the backprop, based on the state after having taken a turn, found before, and the state we saved in the beginning and from now we'll save it at the end of the turn
-                # save the current state again, so we can continue running backprop based on the "previous" turn.
-
-        # NOTE: We need to make a method so that we can take a single turn or at least just pick the next best move, so we know how to evaluate according to TD-learning. Right now, our game just continues in a while loop without nothing to stop it!