implement data collection and part of training

AlphaGo/engine.py

@@ -183,11 +183,15 @@ class GTPEngine():
             return 'unknown player', False
 
     def cmd_get_score(self, args, **kwargs):
-        return self._game.game_engine.executor_get_score(True), None
+        return self._game.game_engine.executor_get_score(True), True
 
     def cmd_show_board(self, args, **kwargs):
         return self._game.board, True
 
+    def cmd_get_prob(self, args, **kwargs):
+        return self._game.prob, True
+
+
 if __name__ == "main":
     game = Game()
     engine = GTPEngine(game_obj=Game)

AlphaGo/game.py

@@ -58,24 +58,9 @@ class Game:
     def set_komi(self, k):
         self.komi = k
 
-    def generate_nn_input(self, latest_boards, color):
-        state = np.zeros([1, self.size, self.size, 17])
-        for i in range(8):
-            state[0, :, :, i] = np.array(np.array(latest_boards[i]) == np.ones(self.size ** 2)).reshape(self.size, self.size)
-            state[0, :, :, i + 8] = np.array(np.array(latest_boards[i]) == -np.ones(self.size ** 2)).reshape(self.size, self.size)
-        if color == utils.BLACK:
-            state[0, :, :, 16] = np.ones([self.size, self.size])
-        if color == utils.WHITE:
-            state[0, :, :, 16] = np.zeros([self.size, self.size])
-        return state
-
     def think(self, latest_boards, color):
-        # TODO : using copy is right, or should we change to deepcopy?
+        mcts = MCTS(self.game_engine, self.evaluator, [latest_boards, color], self.size ** 2 + 1, inverse=True)
-        self.game_engine.simulate_latest_boards = copy.copy(latest_boards)
+        mcts.search(max_step=1)
-        self.game_engine.simulate_board = copy.copy(latest_boards[-1])
-        nn_input = self.generate_nn_input(self.game_engine.simulate_latest_boards, color)
-        mcts = MCTS(self.game_engine, self.evaluator, [self.game_engine.simulate_latest_boards, color], self.size ** 2 + 1, inverse=True)
-        mcts.search(max_step=5)
         temp = 1
         prob = mcts.root.N ** temp / np.sum(mcts.root.N ** temp)
         choice = np.random.choice(self.size ** 2 + 1, 1, p=prob).tolist()[0]

AlphaGo/model.py

@@ -1,6 +1,7 @@
 import os
 import time
 import sys
+import cPickle
 
 import numpy as np
 import tensorflow as tf
@@ -167,4 +168,19 @@ class ResNet(object):
 
     #TODO: design the interface between the environment and training
     def train(self, mode='memory', *args, **kwargs):
+        if mode == 'memory':
             pass
+        if mode == 'file':
+            self.train_with_file(data_path=kwargs['data_path'], checkpoint_path=kwargs['checkpoint_path'])
+
+    def train_with_file(self, data_path, checkpoint_path):
+        if not os.path.exists(data_path):
+            raise ValueError("{} doesn't exist".format(data_path))
+
+        file_list = os.listdir(data_path)
+        if file_list <= 50:
+            time.sleep(1)
+        else:
+            file_list.sort(key=lambda file: os.path.getmtime(data_path + file) if not os.path.isdir(
+                data_path + file) else 0)
+

AlphaGo/play.py

@@ -5,6 +5,18 @@ import re
 import Pyro4
 import time
 import os
+import cPickle
+
+
+class Data(object):
+    def __init__(self):
+        self.boards = []
+        self.probs = []
+        self.winner = 0
+
+    def reset(self):
+        self.__init__()
+
 
 if __name__ == '__main__':
     """
@@ -13,10 +25,13 @@ if __name__ == '__main__':
     """
     # TODO : we should set the network path in a more configurable way.
     parser = argparse.ArgumentParser()
+    parser.add_argument("--result_path", type=str, default="./data/")
     parser.add_argument("--black_weight_path", type=str, default=None)
     parser.add_argument("--white_weight_path", type=str, default=None)
     args = parser.parse_args()
 
+    if not os.path.exists(args.result_path):
+        os.mkdir(args.result_path)
     # black_weight_path = "./checkpoints"
     # white_weight_path = "./checkpoints_origin"
     if args.black_weight_path is not None and (not os.path.exists(args.black_weight_path)):
@@ -35,10 +50,12 @@ if __name__ == '__main__':
     time.sleep(1)
 
     # start two different player with different network weights.
-    agent_v0 = subprocess.Popen(['python', '-u', 'player.py', '--role=black', '--checkpoint_path=' + str(args.black_weight_path)],
+    agent_v0 = subprocess.Popen(
+        ['python', '-u', 'player.py', '--role=black', '--checkpoint_path=' + str(args.black_weight_path)],
         stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
 
-    agent_v1 = subprocess.Popen(['python', '-u', 'player.py', '--role=white', '--checkpoint_path=' + str(args.white_weight_path)],
+    agent_v1 = subprocess.Popen(
+        ['python', '-u', 'player.py', '--role=white', '--checkpoint_path=' + str(args.white_weight_path)],
         stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
 
     server_list = ""
@@ -50,6 +67,7 @@ if __name__ == '__main__':
     print "Start black player at : " + str(agent_v0.pid)
     print "Start white player at : " + str(agent_v1.pid)
 
+    data = Data()
     player = [None] * 2
     player[0] = Pyro4.Proxy("PYRONAME:black")
     player[1] = Pyro4.Proxy("PYRONAME:white")
@@ -63,7 +81,8 @@ if __name__ == '__main__':
 
     evaluate_rounds = 1
     game_num = 0
-    while game_num < evaluate_rounds:
+    try:
+        while True:
             num = 0
             pass_flag = [False, False]
             print("Start game {}".format(game_num))
@@ -89,13 +108,31 @@ if __name__ == '__main__':
                     for j in range(size):
                         print show[board[i * size + j]] + " ",
                     print "\n",
-
+                data.boards.append(board)
+                prob = player[turn].run_cmd(str(num) + ' get_prob')
+                data.probs.append(prob)
             score = player[turn].run_cmd(str(num) + ' get_score')
             print "Finished : ", score.split(" ")[1]
+            # TODO: generalize the player
+            if score > 0:
+                data.winner = 1
+            if score < 0:
+                data.winner = -1
             player[0].run_cmd(str(num) + ' clear_board')
             player[1].run_cmd(str(num) + ' clear_board')
+            file_list = os.listdir(args.result_path)
+            if not file_list:
+                data_num = 0
+            else:
+                file_list.sort(key=lambda file: os.path.getmtime(args.result_path + file) if not os.path.isdir(
+                    args.result_path + file) else 0)
+                data_num = eval(file_list[-1][:-4]) + 1
+                print(file_list)
+            with open("./data/" + str(data_num) + ".pkl", "w") as file:
+                picklestring = cPickle.dump(data, file)
+            data.reset()
             game_num += 1
-
+    except KeyboardInterrupt:
         subprocess.call(["kill", "-9", str(agent_v0.pid)])
         subprocess.call(["kill", "-9", str(agent_v1.pid)])
         print "Kill all player, finish all game."

AlphaGo/player.py

@@ -20,6 +20,7 @@ class Player(object):
         #return "inside the Player of player.py"
         return self.engine.run_cmd(command)
 
+
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument("--checkpoint_path", type=str, default=None)