Merge branch 'master' of gitfub.space:Pownie/backgammon

app.py

@@ -0,0 +1,135 @@
+from flask import Flask, request, jsonify
+from flask_json import FlaskJSON, as_json_p
+from flask_cors import CORS
+from board import Board
+import main
+import random
+from network import Network
+
+app = Flask(__name__)
+
+
+app.config['JSON_ADD_STATUS'] = False
+app.config['JSON_JSONP_OPTIONAL'] = False
+
+json = FlaskJSON(app)
+CORS(app)
+
+config = main.config.copy()
+config['model'] = "player_testings"
+config['ply'] = "1"
+config['board_representation'] = 'quack-fat'
+network = Network(config, config['model'])
+
+network.restore_model()
+
+
+def calc_move_sets(from_board, roll, player):
+    board = from_board
+    sets = []
+    total = 0
+    for r in roll:
+        # print("Value of r:", r)
+        sets.append([Board.calculate_legal_states(board, player, [r, 0]), r])
+        total += r
+    sets.append([Board.calculate_legal_states(board, player, roll), total])
+    return sets
+
+
+def tmp_name(from_board, to_board, roll, player, total_moves, is_quad=False):
+    sets = calc_move_sets(from_board, roll, player)
+    return_board = from_board
+    print("To board:\n",to_board)
+    print("All sets:\n",sets)
+    for idx, board_set in enumerate(sets):
+        board_set[0] = list(board_set[0])
+        # print(to_board)
+        # print(board_set)
+        if to_board in board_set[0]:
+            # print("To board:", to_board)
+            # print(board_set[0])
+            # print(board_set[1])
+            total_moves -= board_set[1]
+            # if it's not the sum of the moves
+            if idx < (4 if is_quad else 2):
+                roll[idx] = 0
+            else:
+                roll = [0, 0]
+            return_board = to_board
+            break
+
+    # print("Return board!:\n",return_board)
+    return total_moves, roll, return_board
+
+def calc_move_stuff(from_board, to_board, roll, player, total_roll, is_quad):
+
+    total_moves, roll, board = tmp_name(from_board, to_board, list(roll), player, total_roll, is_quad)
+    return board, total_moves, roll
+
+
+@app.route('/get_board', methods=['GET'])
+@as_json_p
+def get_board():
+    return {'board':'0, 2, 0, 0, 0, 0, -5, 0, -3, 0, 0, 0, 5, -5, 0, 0, 0, 3, 0, 5, 0, 0, 0, 0, -2, 0'}
+
+
+
+def check_move(prev, curr):
+
+    # TODO: Decide on player system and implement roll properly
+    legal_states = Board.calculate_legal_states(tuple(prev), -1, [1,2])
+
+    truth_list = [list(curr) == list(ele) for ele in legal_states]
+
+    return any(truth_list)
+
+
+
+@app.route('/bot_move', methods=['POST'])
+def bot_move():
+    data = request.get_json(force=True)
+
+    board = [int(x) for x in data['board'].split(',')]
+
+    roll = (random.randrange(1,7), random.randrange(1,7))
+    # print(roll)
+    board, _ = network.make_move(tuple(board), roll, 1)
+    # print("Boards!:",board)
+
+    return ",".join([str(x) for x in list(board)])
+
+
+
+@app.route('/post_board', methods=['POST'])
+def post_board():
+    data = request.get_json(force=True)
+
+    # TODO: Fix hardcoded player
+    player = -1
+
+    board = [int(x) for x in data['board'].split(',')]
+    prev_board = [int(x) for x in data['prevBoard'].split(',')]
+    print(data['roll'])
+    roll = [int(x) for x in data['roll'].split(',')]
+    print(roll)
+    quad = data['quad'] == "true"
+
+
+    # print(board)
+
+    total_roll = int(data['totalRoll'])
+    print("total roll is:", total_roll)
+    return_board, total_moves, roll = calc_move_stuff(tuple(prev_board), tuple(board), tuple(roll), player, total_roll, quad)
+
+    str_board = ",".join([str(x) for x in return_board])
+    str_roll = ",".join([str(x) for x in roll])
+
+
+    return_string = str_board + "#" + str(total_moves) + "#" + str_roll
+
+    print(return_string)
+
+    return return_string
+
+if __name__ == '__main__':
+    app.run(host = '0.0.0.0', port=35270)

bin/0-ply-tests.rb

@@ -0,0 +1,78 @@
+def run_stuff(board_rep, model_name, ply)
+  epi_count = 0
+  system("python3 main.py --train --model #{model_name} --board-rep #{board_rep} --episodes 1 --ply #{ply}")
+  while epi_count < 200000 do
+    system("python3 main.py --eval --model #{model_name} --eval-methods dumbeval --episodes 250 --ply #{ply} --repeat-eval 3")
+    system("python3 main.py --eval --model #{model_name} --eval-methods pubeval --episodes 250 --ply #{ply} --repeat-eval 3")
+    system("python3 main.py --train --model #{model_name} --episodes 2000 --ply #{ply}")
+    epi_count += 2000
+  end
+end
+
+
+### ///////////////////////////////////////////////////////////////
+# QUACK TESTINGS
+### ///////////////////////////////////////////////////////////////
+
+board_rep = "quack"
+model_name = "quack_test_0_ply"
+ply = 0
+
+run_stuff(board_rep, model_name, ply)
+
+
+# board_rep = "quack"
+# model_name = "quack_test_1_ply"
+# ply = 1
+
+# run_stuff(board_rep, model_name, ply)
+
+### ///////////////////////////////////////////////////////////////
+# QUACK-FAT TESTING
+### ///////////////////////////////////////////////////////////////
+
+board_rep = "quack-fat"
+model_name = "quack-fat_test_0_ply"
+ply = 0
+
+run_stuff(board_rep, model_name, ply)
+
+# board_rep = "quack-fat"
+# model_name = "quack-fat_test_1_ply"
+# ply = 1
+
+# run_stuff(board_rep, model_name, ply)
+
+### ///////////////////////////////////////////////////////////////
+# QUACK-NORM TESTING
+### ///////////////////////////////////////////////////////////////
+
+
+board_rep = "quack-norm"
+model_name = "quack-norm_test_0_ply"
+ply = 1
+
+run_stuff(board_rep, model_name, ply)
+
+# board_rep = "quack-norm"
+# model_name = "quack-norm_test_1_ply"
+# ply = 1
+
+# run_stuff(board_rep, model_name, ply)
+
+### ///////////////////////////////////////////////////////////////
+# TESAURO TESTING
+### ///////////////////////////////////////////////////////////////
+
+
+board_rep = "tesauro"
+model_name = "tesauro_test_0_ply"
+ply = 1
+
+run_stuff(board_rep, model_name, ply)
+
+# board_rep = "tesauro"
+# model_name = "tesauro_test_1_ply"
+# ply = 1
+
+# run_stuff(board_rep, model_name, ply)

main.py

@@ -77,15 +77,6 @@ if not os.path.isdir(model_path()):
 if not os.path.isdir(log_path):
     os.mkdir(log_path)
 
-
-def save_config():
-    import yaml
-    # checkpoint_path = os.path.join(config['model_storage_path'], config['model'])
-    # config_path = os.path.join(checkpoint_path, 'config')
-    # with open(config_path, 'a+') as f:
-    #    print("lol")
-    print(yaml.dump(config))
-
 # Define helper functions
 def log_train_outcome(outcome, diff_in_values, trained_eps = 0, log_path = os.path.join(model_path(), 'logs', "train.log")):
     format_vars = { 'trained_eps': trained_eps,
@@ -173,7 +164,6 @@ if __name__ == "__main__":
     # Set up network
     from network import Network
 
-    save_config()
     # Set up variables
     episode_count = config['episode_count']
 

network.py

@@ -93,7 +93,7 @@ class Network:
         :param decay_steps: The amount of steps between each decay
         :return: The result of the exponential decay performed on the learning rate
         """
-        res = max_lr * decay_rate**(global_step // decay_steps)
+        res = max_lr * decay_rate ** (global_step // decay_steps)
         return res
 
     def do_backprop(self, prev_state, value_next):
@@ -104,9 +104,9 @@ class Network:
         :return: Nothing, the calculation is performed on the model of the network
         """
         self.learning_rate = tf.maximum(self.min_learning_rate,
-                                         self.exp_decay(self.max_learning_rate, self.global_step, 0.96, 50000),
+                                        self.exp_decay(self.max_learning_rate, self.global_step, 0.96, 50000),
-                                         name="learning_rate")
+                                        name="learning_rate")
-
+  
         with tf.GradientTape() as tape:
             value = self.model(prev_state.reshape(1,-1))
         grads = tape.gradient(value, self.model.variables)
@@ -165,16 +165,14 @@ class Network:
         :param states: A number of states. The states have to be transformed before being given to this function.
         :return:
         """
-        values = self.model.predict_on_batch(states)
+        return self.model.predict_on_batch(states)
-        return values
 
 
     def restore_model(self):
         """
         Restore a model for a session, such that a trained model and either be further trained or
         used for evaluation
-
+        
-        :param sess: Current session
         :return: Nothing. It's a side-effect that a model gets restored for the network.
         """
 
@@ -211,7 +209,6 @@ class Network:
         and then picking the best, by using the network to evaluate each state. This is 0-ply, ie. no look-ahead.
         The highest score is picked for the 1-player and the max(1-score) is picked for the -1-player.
 
-        :param sess:
         :param board: Current board
         :param roll:  Current roll
         :param player: Current player
@@ -224,10 +221,9 @@ class Network:
         transformed_scores = [x if np.sign(player) > 0 else 1 - x for x in scores]
 
         best_score_idx = np.argmax(np.array(transformed_scores))
-        best_move = legal_moves[best_score_idx]
+        best_move, best_score = legal_moves[best_score_idx], scores[best_score_idx]
-        best_score = scores[best_score_idx]
 
-        return [best_move, best_score]
+        return (best_move, best_score)
 
     def make_move_1_ply(self, board, roll, player):
         """
@@ -237,9 +233,9 @@ class Network:
         :param player:
         :return:
         """
-        # start = time.time()
+        start = time.time()
         best_pair = self.calculate_1_ply(board, roll, player)
-        # print(time.time() - start)
+        #print(time.time() - start)
         return best_pair
 
 
@@ -248,35 +244,30 @@ class Network:
         Find the best move based on a 1-ply look-ahead. First the x best moves are picked from a 0-ply and then
         all moves and scores are found for them. The expected score is then calculated for each of the boards from the
         0-ply.
-        :param sess:
+
         :param board:
         :param roll: The original roll
         :param player: The current player
         :return: Best possible move based on 1-ply look-ahead
-
         """
 
         # find all legal states from the given board and the given roll
         init_legal_states = Board.calculate_legal_states(board, player, roll)
-
         legal_states = np.array([self.board_trans_func(state, player)[0] for state in init_legal_states])
 
-        scores = self.calc_vals(legal_states)
+        scores = [ score.numpy()
-        scores = [score.numpy() for score in scores]
+                   for score
+                   in  self.calc_vals(legal_states) ]
 
         moves_and_scores = list(zip(init_legal_states, scores))
-
+        sorted_moves_and_scores = sorted(moves_and_scores, key=itemgetter(1), reverse=(player == 1))
-        sorted_moves_and_scores = sorted(moves_and_scores, key=itemgetter(1), reverse=player==1)
+        best_boards = [ x[0] for x in sorted_moves_and_scores[:10] ]
-
-        best_boards = [x[0] for x in sorted_moves_and_scores[:10]]
-
-
 
         scores, trans_scores = self.do_ply(best_boards, player)
 
         best_score_idx = np.array(trans_scores).argmax()
 
-        return [best_boards[best_score_idx], scores[best_score_idx]]
+        return (best_boards[best_score_idx], scores[best_score_idx])
 
     def do_ply(self, boards, player):
         """
@@ -285,7 +276,6 @@ class Network:
         allowing the function to search deeper, which could result in an even larger search space. If we wish
         to have more than 2-ply, this should be fixed, so we could extend this method to allow for 3-ply.
 
-        :param sess:
         :param boards: The boards to try all rolls on
         :param player: The player of the previous ply
         :return: An array of scores where each index describes one of the boards which was given as param
@@ -305,11 +295,11 @@ class Network:
         length_list = []
         test_list = []
         # Prepping of data
-        start= time.time()
+        start = time.time()
         for board in boards:
             length = 0
             for roll in all_rolls:
-                all_states = list(Board.calculate_legal_states(board, player*-1, roll))
+                all_states = Board.calculate_legal_states(board, player*-1, roll)
                 for state in all_states:
                     state = np.array(self.board_trans_func(state, player*-1)[0])
                     test_list.append(state)
@@ -318,148 +308,21 @@ class Network:
 
         # print(time.time() - start)
 
-        start = time.time()
+        # start = time.time()
 
-        all_scores_legit = self.model.predict_on_batch(np.array(test_list))
+        all_scores = self.model.predict_on_batch(np.array(test_list))
 
         split_scores = []
         from_idx = 0
         for length in length_list:
-            split_scores.append(all_scores_legit[from_idx:from_idx+length])
+            split_scores.append(all_scores[from_idx:from_idx+length])
             from_idx += length
 
         means_splits = [tf.reduce_mean(scores) for scores in split_scores]
         transformed_means_splits = [x if player == 1 else (1-x) for x in means_splits]
         # print(time.time() - start)
 
-        return ([means_splits, transformed_means_splits])
+        return (means_splits, transformed_means_splits)
-
-
-    def calc_n_ply(self, n_init, sess, board, player, roll):
-        """
-        :param n_init:
-        :param sess:
-        :param board:
-        :param player:
-        :param roll:
-        :return:
-        """
-
-        # find all legal states from the given board and the given roll
-        init_legal_states = Board.calculate_legal_states(board, player, roll)
-
-        # find all values for the above boards
-        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.
-        sorted_moves_and_scores = sorted(zero_ply_moves_and_scores, key=itemgetter(1), reverse=player==1)
-
-
-        best_boards = [x[0] for x in sorted_moves_and_scores[:10]]
-
-        best_move_score_pair = self.n_ply(n_init, sess, best_boards, player)
-
-        return best_move_score_pair
-
-
-    def n_ply(self, n_init, sess, boards_init, player_init):
-        """
-        :param n_init:
-        :param sess:
-        :param boards_init:
-        :param player_init:
-        :return:
-        """
-        def ply(n, boards, player):
-            def calculate_possible_states(board):
-                possible_rolls = [ (1, 1), (1, 2), (1, 3), (1, 4), (1, 5),
-                                   (1, 6), (2, 2), (2, 3), (2, 4), (2, 5),
-                                   (2, 6), (3, 3), (3, 4), (3, 5), (3, 6),
-                                   (4, 4), (4, 5), (4, 6), (5, 5), (5, 6),
-                                   (6, 6) ]
-
-                # for roll in possible_rolls:
-                #     print(len(Board.calculate_legal_states(board, player, roll)))
-
-                return [ Board.calculate_legal_states(board, player, roll)
-                         for roll
-                         in  possible_rolls ]
-
-            def find_best_state_score(boards):
-                score_pairs = [ (board, self.eval_state(sess, self.board_trans_func(board, player)))
-                                for board
-                                in  boards ]
-                scores = [ pair[1]
-                           for pair
-                           in score_pairs ]
-                best_score_pair = score_pairs[np.array(scores).argmax()]
-
-                return best_score_pair
-
-            def average_score(boards):
-                return sum(boards)/len(boards)
-
-            def average_ply_score(board):
-                states_for_rolls = calculate_possible_states(board)
-
-                best_state_score_for_each_roll = [
-                    find_best_state_score(states)
-                    for states
-                    in  states_for_rolls ]
-                best_score_for_each_roll = [ x[1]
-                                             for x
-                                             in best_state_score_for_each_roll ]
-
-                average_score_var = average_score(best_score_for_each_roll)
-                return average_score_var
-
-
-            if n == 1:
-                average_score_pairs = [ (board, average_ply_score(board))
-                                        for board
-                                        in  boards ]
-                return average_score_pairs
-            elif n > 1: # n != 1
-                def average_for_score_pairs(score_pairs):
-                    scores = [ pair[1]
-                               for pair
-                               in score_pairs ]
-                    return sum(scores)/len(scores)
-
-                def average_plain(scores):
-                    return sum(scores)/len(scores)
-
-                print("+"*20)
-                print(n)
-                print(type(boards))
-                print(boards)
-                possible_states_for_boards = [
-                    (board, calculate_possible_states(board))
-                    for board
-                    in  boards ]
-
-                average_score_pairs = [
-                    (inner_boards[0], average_plain([ average_for_score_pairs(ply(n - 1, inner_board, player * -1 if n == 1 else player))
-                                                      for inner_board
-                                                      in  inner_boards[1] ]))
-                    for inner_boards
-                    in  possible_states_for_boards ]
-
-                return average_score_pairs
-
-            else:
-                assert False
-
-        if n_init < 1: print("Unexpected argument n = {}".format(n_init)); exit()
-
-        boards_with_scores = ply(n_init, boards_init, -1 * player_init)
-        #print("Boards with scores:",boards_with_scores)
-        scores = [ ( pair[1] if player_init == 1 else (1 - pair[1]) )
-                   for pair
-                   in boards_with_scores ]
-        #print("All the scores:",scores)
-        best_score_pair = boards_with_scores[np.array(scores).argmax()]
-        return best_score_pair
 
 
     def eval(self, episode_count, trained_eps = 0):
@@ -477,7 +340,6 @@ class Network:
             """
             Do the actual evaluation
 
-            :param sess:
             :param method:     Either pubeval or dumbeval
             :param episodes:   Amount of episodes to use in the evaluation
             :param trained_eps:
@@ -509,11 +371,9 @@ class Network:
                     board = Board.initial_state
                     while Board.outcome(board) is None:
                         roll = (random.randrange(1, 7), random.randrange(1, 7))
-
                         board = (self.make_move(board, roll, 1))[0]
 
                         roll = (random.randrange(1, 7), random.randrange(1, 7))
-
                         board = Eval.make_pubeval_move(board, -1, roll)[0][0:26]
 
                     sys.stderr.write("\t outcome {}".format(Board.outcome(board)[1]))
@@ -532,11 +392,9 @@ class Network:
                     board = Board.initial_state
                     while Board.outcome(board) is None:
                         roll = (random.randrange(1, 7), random.randrange(1, 7))
-
                         board = (self.make_move(board, roll, 1))[0]
 
                         roll = (random.randrange(1, 7), random.randrange(1, 7))
-
                         board = Eval.make_dumbeval_move(board, -1, roll)[0][0:26]
 
                     sys.stderr.write("\t outcome {}".format(Board.outcome(board)[1]))
@@ -653,7 +511,7 @@ 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)
-
+    
             self.do_backprop(self.board_trans_func(prev_board, player), scaled_final_score.reshape(1,1))
 
             sys.stderr.write("\n")

player.py

@@ -20,21 +20,22 @@ class Player:
             sets.append([Board.calculate_legal_states(board, player, [r,0]), r])
             total += r
         sets.append([Board.calculate_legal_states(board, player, [total,0]), total])
+        print(sets)
         return sets
 
 
-    def tmp_name(self, from_board, to_board, roll, player, total_moves):
+    def tmp_name(self, from_board, to_board, roll, player, total_moves, is_quad = False):
         sets = self.calc_move_sets(from_board, roll, player)
         return_board = from_board
         for idx, board_set in enumerate(sets):
 
             board_set[0] = list(board_set[0])
-            print(to_board)
+            # print(to_board)
-            print(board_set)
+            # print(board_set)
             if to_board in board_set[0]:
                 total_moves -= board_set[1]
                 # if it's not the sum of the moves
-                if idx < 2:
+                if idx < (4 if is_quad else 2):
                     roll[idx] = 0
                 else:
                     roll = [0,0]
@@ -43,8 +44,11 @@ class Player:
         return total_moves, roll, return_board
 
     def make_human_move(self, board, roll):
-        total_moves = roll[0] + roll[1] if roll[0] != roll[1] else int(roll[0])*4
+        is_quad = roll[0] == roll[1]
-        move = ""
+        total_moves = roll[0] + roll[1] if not is_quad else int(roll[0])*4
+        if is_quad:
+            roll = [roll[0]]*4
+        
         while total_moves != 0:
             while True:
                 print("You have {roll} left!".format(roll=total_moves))
@@ -60,6 +64,6 @@ class Player:
                         print("The correct syntax is: 2/5 for a move from index 2 to 5.")
 
             to_board = Board.apply_moves_to_board(board, self.get_sym(), move)
-            total_moves, roll, board = self.tmp_name(board, to_board, list(roll), self.get_sym(), total_moves)
+            total_moves, roll, board = self.tmp_name(board, to_board, list(roll), self.get_sym(), total_moves, is_quad)
             print(Board.pretty(board))
         return board