Merge pull request #3 from sproblvem/double-network

Double network

AlphaGo/game.py

@@ -12,6 +12,7 @@ import numpy as np
 import sys, os
 import model
 from collections import deque
+
 sys.path.append(os.path.join(os.path.dirname(__file__), os.path.pardir))
 from tianshou.core.mcts.mcts import MCTS
 
@@ -19,6 +20,7 @@ import go
 import reversi
 import time
 
+
 class Game:
     '''
     Load the real game and trained weights.
@@ -26,11 +28,9 @@ class Game:
     TODO : Maybe merge with the engine class in future,
     currently leave it untouched for interacting with Go UI.
     '''
-    def __init__(self, name=None, role=None, debug=False, checkpoint_path=None):
+
+    def __init__(self, name=None, debug=False, black_checkpoint_path=None, white_checkpoint_path=None):
         self.name = name
-	if role is None:
-	    raise ValueError("Need a role!")
-        self.role = role
         self.debug = debug
         if self.name == "go":
             self.size = 9
@@ -38,7 +38,7 @@ class Game:
             self.history_length = 8
             self.history = []
             self.history_set = set()
-            self.game_engine = go.Go(size=self.size, komi=self.komi, role=self.role)
+            self.game_engine = go.Go(size=self.size, komi=self.komi)
             self.board = [utils.EMPTY] * (self.size ** 2)
         elif self.name == "reversi":
             self.size = 8
@@ -49,8 +49,9 @@ class Game:
         else:
             raise ValueError(name + " is an unknown game...")
 
-        self.evaluator = model.ResNet(self.size, self.size ** 2 + 1, history_length=self.history_length,
-                                      checkpoint_path=checkpoint_path)
+        self.model = model.ResNet(self.size, self.size ** 2 + 1, history_length=self.history_length,
+                                  black_checkpoint_path=black_checkpoint_path,
+                                  white_checkpoint_path=white_checkpoint_path)
         self.latest_boards = deque(maxlen=self.history_length)
         for _ in range(self.history_length):
             self.latest_boards.append(self.board)
@@ -72,15 +73,22 @@ class Game:
         self.komi = k
 
     def think(self, latest_boards, color):
-        mcts = MCTS(self.game_engine, self.evaluator, [latest_boards, color],
-                    self.size ** 2 + 1, role=self.role, debug=self.debug, inverse=True)
+        if color == utils.BLACK:
+            role = 'black'
+        elif color == utils.WHITE:
+            role = 'white'
+        else:
+            raise ValueError("game.py[think] - unknown color : {}".format(color))
+        evaluator = lambda state:self.model(role, state)
+        mcts = MCTS(self.game_engine, evaluator, [latest_boards, color],
+                    self.size ** 2 + 1, role=role, debug=self.debug, inverse=True)
         mcts.search(max_step=100)
         if self.debug:
             file = open("mcts_debug.log", 'ab')
-            np.savetxt(file, mcts.root.Q, header="\n" + self.role + " Q value : ", fmt='%.4f', newline=", ")
-            np.savetxt(file, mcts.root.W, header="\n" + self.role + " W value : ", fmt='%.4f', newline=", ")
-            np.savetxt(file, mcts.root.N, header="\n" + self.role + " N value : ", fmt="%d", newline=", ")
-            np.savetxt(file, mcts.root.prior, header="\n" + self.role + " prior : ", fmt='%.4f', newline=", ")
+            np.savetxt(file, mcts.root.Q, header="\n" + role + " Q value : ", fmt='%.4f', newline=", ")
+            np.savetxt(file, mcts.root.W, header="\n" + role + " W value : ", fmt='%.4f', newline=", ")
+            np.savetxt(file, mcts.root.N, header="\n" + role + " N value : ", fmt="%d", newline=", ")
+            np.savetxt(file, mcts.root.prior, header="\n" + role + " prior : ", fmt='%.4f', newline=", ")
             file.close()
         temp = 1
         prob = mcts.root.N ** temp / np.sum(mcts.root.N ** temp)
@@ -98,7 +106,8 @@ class Game:
         if self.name == "reversi":
             res = self.game_engine.executor_do_move(self.history, self.latest_boards, self.board, color, vertex)
         if self.name == "go":
-            res = self.game_engine.executor_do_move(self.history, self.history_set, self.latest_boards, self.board, color, vertex)
+            res = self.game_engine.executor_do_move(self.history, self.history_set, self.latest_boards, self.board,
+                                                    color, vertex)
         return res
 
     def think_play_move(self, color):
@@ -128,8 +137,8 @@ class Game:
             print('')
         sys.stdout.flush()
 
+
 if __name__ == "__main__":
-    game = Game(name="reversi", role="black", checkpoint_path=None)
+    game = Game(name="reversi", checkpoint_path=None)
     game.debug = True
     game.think_play_move(utils.BLACK)
-

AlphaGo/go.py

@@ -18,7 +18,6 @@ class Go:
     def __init__(self, **kwargs):
         self.size = kwargs['size']
         self.komi = kwargs['komi']
-        self.role = kwargs['role']
 
     def _flatten(self, vertex):
         x, y = vertex
@@ -332,7 +331,7 @@ class Go:
 
 
 if __name__ == "__main__":
-    go = Go(size=9, komi=3.75, role = utils.BLACK)
+    go = Go(size=9, komi=3.75)
     endgame = [
         1, 0, 1, 0, 1, 1, -1, 0, -1,
         1, 1, 1, 1, 1, 1, -1, -1, -1,

AlphaGo/model.py

@@ -80,7 +80,8 @@ class Data(object):
 
 
 class ResNet(object):
-    def __init__(self, board_size, action_num, history_length=1, residual_block_num=10, checkpoint_path=None):
+    def __init__(self, board_size, action_num, history_length=1, residual_block_num=10, black_checkpoint_path=None,
+                 white_checkpoint_path=None):
         """
         the resnet model
 
@@ -88,25 +89,49 @@ class ResNet(object):
         :param action_num: an integer, number of unique actions at any state
         :param history_length: an integer, the history length to use, default is 1
         :param residual_block_num: an integer, the number of residual block, default is 20, at least 1
-        :param checkpoint_path: a string, the path to the checkpoint, default is None,
+        :param black_checkpoint_path: a string, the path to the black checkpoint, default is None,
+        :param white_checkpoint_path: a string, the path to the white checkpoint, default is None,
         """
         self.board_size = board_size
         self.action_num = action_num
         self.history_length = history_length
-        self.checkpoint_path = checkpoint_path
+        self.black_checkpoint_path = black_checkpoint_path
+        self.white_checkpoint_path = white_checkpoint_path
         self.x = tf.placeholder(tf.float32, shape=[None, self.board_size, self.board_size, 2 * self.history_length + 1])
         self.is_training = tf.placeholder(tf.bool, shape=[])
         self.z = tf.placeholder(tf.float32, shape=[None, 1])
         self.pi = tf.placeholder(tf.float32, shape=[None, self.action_num])
-        self._build_network(residual_block_num, self.checkpoint_path)
+        self._build_network('black', residual_block_num)
+        self._build_network('white', residual_block_num)
+        self.sess = multi_gpu.create_session()
+        self.sess.run(tf.global_variables_initializer())
+        if black_checkpoint_path is not None:
+            ckpt_file = tf.train.latest_checkpoint(black_checkpoint_path)
+            if ckpt_file is not None:
+                print('Restoring model from {}...'.format(ckpt_file))
+                self.black_saver.restore(self.sess, ckpt_file)
+                print('Successfully loaded')
+            else:
+                raise ValueError("No model in path {}".format(black_checkpoint_path))
+
+        if white_checkpoint_path is not None:
+            ckpt_file = tf.train.latest_checkpoint(white_checkpoint_path)
+            if ckpt_file is not None:
+                print('Restoring model from {}...'.format(ckpt_file))
+                self.white_saver.restore(self.sess, ckpt_file)
+                print('Successfully loaded')
+            else:
+                raise ValueError("No model in path {}".format(white_checkpoint_path))
+        self.update = [tf.assign(white_params, black_params) for black_params, white_params in
+                       zip(self.black_var_list, self.white_var_list)]
 
         # training hyper-parameters:
-        self.window_length = 3
+        self.window_length = 900
         self.save_freq = 5000
         self.training_data = {'states': deque(maxlen=self.window_length), 'probs': deque(maxlen=self.window_length),
                               'winner': deque(maxlen=self.window_length), 'length': deque(maxlen=self.window_length)}
 
-    def _build_network(self, residual_block_num, checkpoint_path):
+    def _build_network(self, scope, residual_block_num):
         """
         build the network
 
@@ -114,7 +139,7 @@ class ResNet(object):
         :param checkpoint_path: a string, the path to the checkpoint, if None, use random initialization parameter
         :return: None
         """
-
+        with tf.variable_scope(scope, reuse=tf.AUTO_REUSE):
             h = layers.conv2d(self.x, 256, kernel_size=3, stride=1, activation_fn=tf.nn.relu,
                               normalizer_fn=layers.batch_norm,
                               normalizer_params={'is_training': self.is_training,
@@ -122,31 +147,28 @@ class ResNet(object):
                               weights_regularizer=layers.l2_regularizer(1e-4))
             for i in range(residual_block_num - 1):
                 h = residual_block(h, self.is_training)
-        self.v = value_head(h, self.is_training)
-        self.p = policy_head(h, self.is_training, self.action_num)
-        self.prob = tf.nn.softmax(self.p)
-        self.value_loss = tf.reduce_mean(tf.square(self.z - self.v))
-        self.policy_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=self.pi, logits=self.p))
+            self.__setattr__(scope + '_v', value_head(h, self.is_training))
+            self.__setattr__(scope + '_p', policy_head(h, self.is_training, self.action_num))
+        self.__setattr__(scope + '_prob', tf.nn.softmax(self.__getattribute__(scope + '_p')))
+        self.__setattr__(scope + '_value_loss', tf.reduce_mean(tf.square(self.z - self.__getattribute__(scope + '_v'))))
+        self.__setattr__(scope + '_policy_loss',
+                         tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=self.pi,
+                                                                                logits=self.__getattribute__(
+                                                                                    scope + '_p'))))
 
-        self.reg = tf.add_n(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))
-        self.total_loss = self.value_loss + self.policy_loss + self.reg
-        self.update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
-        with tf.control_dependencies(self.update_ops):
-            self.train_op = tf.train.AdamOptimizer(1e-4).minimize(self.total_loss)
-        self.var_list = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
-        self.saver = tf.train.Saver(max_to_keep=0, var_list=self.var_list)
-        self.sess = multi_gpu.create_session()
-        self.sess.run(tf.global_variables_initializer())
-        if checkpoint_path is not None:
-            ckpt_file = tf.train.latest_checkpoint(checkpoint_path)
-            if ckpt_file is not None:
-                print('Restoring model from {}...'.format(ckpt_file))
-                self.saver.restore(self.sess, ckpt_file)
-                print('Successfully loaded')
-            else:
-                raise ValueError("No model in path {}".format(checkpoint_path))
+        self.__setattr__(scope + '_reg', tf.add_n(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES, scope=scope)))
+        self.__setattr__(scope + '_total_loss', self.__getattribute__(scope + '_value_loss') + self.__getattribute__(
+            scope + '_policy_loss') + self.__getattribute__(scope + '_reg'))
+        self.__setattr__(scope + '_update_ops', tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope=scope))
+        self.__setattr__(scope + '_var_list', tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=scope))
+        with tf.control_dependencies(self.__getattribute__(scope + '_update_ops')):
+            self.__setattr__(scope + '_train_op',
+                             tf.train.AdamOptimizer(1e-4).minimize(self.__getattribute__(scope + '_total_loss'),
+                                                                   var_list=self.__getattribute__(scope + '_var_list')))
+        self.__setattr__(scope + '_saver',
+                         tf.train.Saver(max_to_keep=0, var_list=self.__getattribute__(scope + '_var_list')))
 
-    def __call__(self, state):
+    def __call__(self, role, state):
         """
 
         :param history: a list, the history
@@ -154,15 +176,20 @@ class ResNet(object):
         :return: a list of tensor, the predicted value and policy given the history and color
         """
         # Note : maybe we can use it for isolating test of MCTS
-        #prob = [1.0 / self.action_num] * self.action_num
-        #return [prob, np.random.uniform(-1, 1)]
+        # prob = [1.0 / self.action_num] * self.action_num
+        # return [prob, np.random.uniform(-1, 1)]
         history, color = state
         if len(history) != self.history_length:
             raise ValueError(
                 'The length of history cannot meet the need of the model, given {}, need {}'.format(len(history),
                                                                                                     self.history_length))
         eval_state = self._history2state(history, color)
-        return self.sess.run([self.prob, self.v], feed_dict={self.x: eval_state, self.is_training: False})
+        if role == 'black':
+            return self.sess.run([self.black_prob, self.black_v],
+                                 feed_dict={self.x: eval_state, self.is_training: False})
+        if role == 'white':
+            return self.sess.run([self.white_prob, self.white_v],
+                                 feed_dict={self.x: eval_state, self.is_training: False})
 
     def _history2state(self, history, color):
         """
@@ -174,10 +201,12 @@ class ResNet(object):
         """
         state = np.zeros([1, self.board_size, self.board_size, 2 * self.history_length + 1])
         for i in range(self.history_length):
-            state[0, :, :, i] = np.array(np.array(history[i]).flatten() == np.ones(self.board_size ** 2)).reshape(self.board_size,
+            state[0, :, :, i] = np.array(np.array(history[i]).flatten() == np.ones(self.board_size ** 2)).reshape(
+                self.board_size,
                 self.board_size)
             state[0, :, :, i + self.history_length] = np.array(
-                np.array(history[i]).flatten() == -np.ones(self.board_size ** 2)).reshape(self.board_size, self.board_size)
+                np.array(history[i]).flatten() == -np.ones(self.board_size ** 2)).reshape(self.board_size,
+                                                                                          self.board_size)
         # TODO: need a config to specify the BLACK and WHITE
         if color == +1:
             state[0, :, :, 2 * self.history_length] = np.ones([self.board_size, self.board_size])
@@ -187,19 +216,27 @@ class ResNet(object):
 
     # TODO: design the interface between the environment and training
     def train(self, mode='memory', *args, **kwargs):
+        """
+        The method to train the network
+
+        :param target: a string, which to optimize, can only be "both", "black" and "white"
+        :param mode: a string, how to optimize, can only be "memory" and "file"
+        """
         if mode == 'memory':
             pass
         if mode == 'file':
             self._train_with_file(data_path=kwargs['data_path'], batch_size=kwargs['batch_size'],
-                                  checkpoint_path=kwargs['checkpoint_path'])
+                                  save_path=kwargs['save_path'])
 
-    def _train_with_file(self, data_path, batch_size, checkpoint_path):
+    def _train_with_file(self, data_path, batch_size, save_path):
         # check if the path is valid
         if not os.path.exists(data_path):
             raise ValueError("{} doesn't exist".format(data_path))
-        self.checkpoint_path = checkpoint_path
-        if not os.path.exists(self.checkpoint_path):
-            os.mkdir(self.checkpoint_path)
+        self.save_path = save_path
+        if not os.path.exists(self.save_path):
+            os.mkdir(self.save_path)
+            os.mkdir(self.save_path + 'black')
+            os.mkdir(self.save_path + 'white')
 
         new_file_list = []
         all_file_list = []
@@ -227,7 +264,8 @@ class ResNet(object):
             else:
                 start_time = time.time()
                 for i in range(batch_size):
-                    priority = np.array(self.training_data['length']) / (0.0 + np.sum(np.array(self.training_data['length'])))
+                    priority = np.array(self.training_data['length']) / (
+                            0.0 + np.sum(np.array(self.training_data['length'])))
                     game_num = np.random.choice(self.window_length, 1, p=priority)[0]
                     state_num = np.random.randint(self.training_data['length'][game_num])
                     rotate_times = np.random.randint(4)
@@ -237,11 +275,15 @@ class ResNet(object):
                         self._preprocession(self.training_data['states'][game_num][state_num], reflect_times,
                                             reflect_orientation, rotate_times))
                     training_data['probs'].append(np.concatenate(
-                        [self._preprocession(self.training_data['probs'][game_num][state_num][:-1].reshape(self.board_size, self.board_size, 1), reflect_times,
-                                            reflect_orientation, rotate_times).reshape(1, self.board_size**2), self.training_data['probs'][game_num][state_num][-1].reshape(1,1)], axis=1))
+                        [self._preprocession(
+                            self.training_data['probs'][game_num][state_num][:-1].reshape(self.board_size,
+                                                                                          self.board_size, 1),
+                            reflect_times,
+                            reflect_orientation, rotate_times).reshape(1, self.board_size ** 2),
+                         self.training_data['probs'][game_num][state_num][-1].reshape(1, 1)], axis=1))
                     training_data['winner'].append(self.training_data['winner'][game_num][state_num].reshape(1, 1))
                 value_loss, policy_loss, reg, _ = self.sess.run(
-                    [self.value_loss, self.policy_loss, self.reg, self.train_op],
+                    [self.black_value_loss, self.black_policy_loss, self.black_reg, self.black_train_op],
                     feed_dict={self.x: np.concatenate(training_data['states'], axis=0),
                                self.z: np.concatenate(training_data['winner'], axis=0),
                                self.pi: np.concatenate(training_data['probs'], axis=0),
@@ -252,8 +294,11 @@ class ResNet(object):
                                                                                                  value_loss,
                                                                                                  policy_loss, reg))
                 if iters % self.save_freq == 0:
-                    save_path = "Iteration{}.ckpt".format(iters)
-                    self.saver.save(self.sess, self.checkpoint_path + save_path)
+                    ckpt_file = "Iteration{}.ckpt".format(iters)
+                    self.black_saver.save(self.sess, self.save_path + 'black/' + ckpt_file)
+                    self.sess.run(self.update)
+                    self.white_saver.save(self.sess, self.save_path + 'white/' + ckpt_file)
+
                 for key in training_data.keys():
                     training_data[key] = []
                 iters += 1
@@ -342,5 +387,5 @@ class ResNet(object):
 
 
 if __name__ == "__main__":
-    model = ResNet(board_size=9, action_num=82, history_length=8)
-    model.train("file", data_path="./data/", batch_size=128, checkpoint_path="./checkpoint/")
+    model = ResNet(board_size=8, action_num=65, history_length=1, black_checkpoint_path="./checkpoint/black", white_checkpoint_path="./checkpoint/white")
+    model.train(mode="file", data_path="./data/", batch_size=128, save_path="./checkpoint/")

AlphaGo/play.py

@@ -1,10 +1,10 @@
 import argparse
-import subprocess
 import sys
 import re
-import Pyro4
 import time
 import os
+from game import Game
+from engine import GTPEngine
 import utils
 from time import gmtime, strftime
 
@@ -24,6 +24,7 @@ class Data(object):
     def reset(self):
         self.__init__()
 
+
 if __name__ == '__main__':
     """
     Starting two different players which load network weights to evaluate the winning ratio.
@@ -47,65 +48,13 @@ if __name__ == '__main__':
     if args.white_weight_path is not None and (not os.path.exists(args.white_weight_path)):
         raise ValueError("Can't find the network weights for white player.")
 
-    # kill the old server
-    # kill_old_server = subprocess.Popen(['killall', 'pyro4-ns'])
-    # print "kill the old pyro4 name server, the return code is : " + str(kill_old_server.wait())
-    # time.sleep(1)
-
-    # start a name server if no name server exists
-    if len(os.popen('ps aux | grep pyro4-ns | grep -v grep').readlines()) == 0:
-        start_new_server = subprocess.Popen(['pyro4-ns', '&'])
-        print("Start Name Sever : " + str(start_new_server.pid))  # + str(start_new_server.wait())
-        time.sleep(1)
-
-    # start two different player with different network weights.
-    server_list = subprocess.check_output(['pyro4-nsc', 'list'])
-    current_time = strftime("%Y%m%d_%H%M%S", gmtime())
-
-    black_role_name = 'black' + current_time
-    white_role_name = 'white' + current_time
-
-    black_player = subprocess.Popen(
-        ['python', '-u', 'player.py', '--game=' + args.game, '--role=' + black_role_name,
-         '--checkpoint_path=' + str(args.black_weight_path), '--debug=' + str(args.debug)],
-        stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
-    bp_output = black_player.stdout.readline()
-    bp_message = bp_output
-    # '' means player.py failed to start, "Start requestLoop" means player.py start successfully
-    while bp_output != '' and "Start requestLoop" not in bp_output:
-        bp_output = black_player.stdout.readline()
-        bp_message += bp_output
-    print("============ " + black_role_name + " message ============" + "\n" + bp_message),
-
-    white_player = subprocess.Popen(
-        ['python', '-u', 'player.py', '--game=' + args.game, '--role=' + white_role_name,
-         '--checkpoint_path=' + str(args.white_weight_path), '--debug=' + str(args.debug)],
-        stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
-    wp_output = white_player.stdout.readline()
-    wp_message = wp_output
-    while wp_output != '' and "Start requestLoop" not in wp_output:
-        wp_output = white_player.stdout.readline()
-        wp_message += wp_output
-    print("============ " + white_role_name + " message ============" + "\n" + wp_message),
-
-    server_list = ""
-    while (black_role_name not in server_list) or (white_role_name not in server_list):
-        if python_version < (3, 0):
-            # TODO : @renyong what is the difference between those two options?
-            server_list = subprocess.check_output(['pyro4-nsc', 'list'])
-        else:
-            server_list = subprocess.check_output(['pyro4-nsc', 'list'])
-        print("Waiting for the server start...")
-        time.sleep(1)
-    print(server_list)
-    print("Start black player at : " + str(black_player.pid))
-    print("Start white player at : " + str(white_player.pid))
+    game = Game(name=args.game,
+                black_checkpoint_path=args.black_weight_path,
+                white_checkpoint_path=args.white_weight_path,
+                debug=args.debug)
+    engine = GTPEngine(game_obj=game, name='tianshou', version=0)
 
     data = Data()
-    player = [None] * 2
-    player[0] = Pyro4.Proxy("PYRONAME:" + black_role_name)
-    player[1] = Pyro4.Proxy("PYRONAME:" + white_role_name)
-
     role = ["BLACK", "WHITE"]
     color = ['b', 'w']
 
@@ -118,7 +67,7 @@ if __name__ == '__main__':
     game_num = 0
     try:
         while True:
-        # while game_num < evaluate_rounds:
+        #while game_num < evaluate_rounds:
             start_time = time.time()
             num = 0
             pass_flag = [False, False]
@@ -126,7 +75,7 @@ if __name__ == '__main__':
             # end the game if both palyer chose to pass, or play too much turns
             while not (pass_flag[0] and pass_flag[1]) and num < size[args.game] ** 2 * 2:
                 turn = num % 2
-                board = player[turn].run_cmd(str(num) + ' show_board')
+                board = engine.run_cmd(str(num) + ' show_board')
                 board = eval(board[board.index('['):board.index(']') + 1])
                 for i in range(size[args.game]):
                     for j in range(size[args.game]):
@@ -134,7 +83,7 @@ if __name__ == '__main__':
                     print "\n",
                 data.boards.append(board)
                 start_time = time.time()
-                move = player[turn].run_cmd(str(num) + ' genmove ' + color[turn])[:-1]
+                move = engine.run_cmd(str(num) + ' genmove ' + color[turn])[:-1]
                 print("\n" + role[turn] + " : " + str(move)),
                 num += 1
                 match = re.search(pattern, move)
@@ -146,21 +95,19 @@ if __name__ == '__main__':
                     # print "no match"
                     play_or_pass = ' PASS'
                     pass_flag[turn] = True
-                result = player[1 - turn].run_cmd(str(num) + ' play ' + color[turn] + ' ' + play_or_pass + '\n')
-                prob = player[turn].run_cmd(str(num) + ' get_prob')
+                prob = engine.run_cmd(str(num) + ' get_prob')
                 prob = space.sub(',', prob[prob.index('['):prob.index(']') + 1])
                 prob = prob.replace('[,', '[')
                 prob = prob.replace('],', ']')
                 prob = eval(prob)
                 data.probs.append(prob)
-            score = player[0].run_cmd(str(num) + ' get_score')
+            score = engine.run_cmd(str(num) + ' get_score')
             print("Finished : {}".format(score.split(" ")[1]))
             if eval(score.split(" ")[1]) > 0:
                 data.winner = utils.BLACK
             if eval(score.split(" ")[1]) < 0:
                 data.winner = utils.WHITE
-            player[0].run_cmd(str(num) + ' clear_board')
-            player[1].run_cmd(str(num) + ' clear_board')
+            engine.run_cmd(str(num) + ' clear_board')
             file_list = os.listdir(args.data_path)
             current_time = strftime("%Y%m%d_%H%M%S", gmtime())
             if os.path.exists(args.data_path + current_time + ".pkl"):
@@ -172,7 +119,3 @@ if __name__ == '__main__':
             game_num += 1
     except KeyboardInterrupt:
         pass
-
-    subprocess.call(["kill", "-9", str(black_player.pid)])
-    subprocess.call(["kill", "-9", str(white_player.pid)])
-    print("Kill all player, finish all game.")

AlphaGo/player.py

@@ -1,42 +0,0 @@
-import argparse
-import Pyro4
-from game import Game
-from engine import GTPEngine
-
-@Pyro4.expose
-class Player(object):
-    """
-    This is the class which defines the object called by Pyro4 (Python remote object).
-    It passes the command to our engine, and return the result.
-    """
-    def __init__(self, **kwargs):
-        self.role = kwargs['role']
-        self.engine = kwargs['engine']
-
-    def run_cmd(self, command):
-        return self.engine.run_cmd(command)
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--checkpoint_path", type=str, default="None")
-    parser.add_argument("--role", type=str, default="unknown")
-    parser.add_argument("--debug", type=str, default="False")
-    parser.add_argument("--game", type=str, default="go")
-    args = parser.parse_args()
-    if args.checkpoint_path == 'None':
-        args.checkpoint_path = None
-    game = Game(name=args.game, role=args.role,
-                checkpoint_path=args.checkpoint_path,
-                debug=eval(args.debug))
-    engine = GTPEngine(game_obj=game, name='tianshou', version=0)
-
-    daemon = Pyro4.Daemon()                # make a Pyro daemon
-    ns = Pyro4.locateNS()                  # find the name server
-    player = Player(role=args.role, engine=engine)
-    print("Init " + args.role + " player finished")
-    uri = daemon.register(player)          # register the greeting maker as a Pyro object
-    print("Start on name " + args.role)
-    ns.register(args.role, uri)            # register the object with a name in the name server
-    print("Start requestLoop " + str(uri))
-    daemon.requestLoop()                   # start the event loop of the server to wait for calls
-