fix bug of game config and add profing functions to mcts
This commit is contained in:
Dong Yan
parent
2d9aa32758
commit
f0074aa7ca
@ -198,5 +198,4 @@ class GTPEngine():
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if __name__ == "main":
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game = Game()
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engine = GTPEngine(game_obj=game)
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print ("test engine.py")
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@ -26,7 +26,7 @@ class Game:
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TODO : Maybe merge with the engine class in future,
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currently leave it untouched for interacting with Go UI.
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'''
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def __init__(self, name="reversi", role="unknown", debug=False, checkpoint_path=None):
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def __init__(self, name=None, role=None, debug=False, checkpoint_path=None):
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self.name = name
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self.role = role
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self.debug = debug
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@ -119,10 +119,7 @@ class Game:
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sys.stdout.flush()
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if __name__ == "__main__":
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g = Game("go")
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print(g.board)
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g.clear()
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g.think_play_move(1)
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print("test game.py")
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#file = open("debug.txt", "a")
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#file.write("mcts check\n")
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#file.close()
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@ -60,13 +60,14 @@ if __name__ == '__main__':
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black_role_name = 'black' + str(args.id)
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white_role_name = 'white' + str(args.id)
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game_name = 'go'
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agent_v0 = subprocess.Popen(
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['python', '-u', 'player.py', '--role=' + black_role_name,
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['python', '-u', 'player.py', '--game=' + game_name, '--role=' + black_role_name,
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'--checkpoint_path=' + str(args.black_weight_path), '--debug=' + str(args.debug)],
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stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
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agent_v1 = subprocess.Popen(
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['python', '-u', 'player.py', '--role=' + white_role_name,
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['python', '-u', 'player.py', '--game=' + game_name, '--role=' + white_role_name,
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'--checkpoint_path=' + str(args.black_weight_path), '--debug=' + str(args.debug)],
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stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
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@ -102,13 +103,13 @@ if __name__ == '__main__':
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pass_flag = [False, False]
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print("Start game {}".format(game_num))
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# end the game if both palyer chose to pass, or play too much turns
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while not (pass_flag[0] and pass_flag[1]) and num < size["reversi"] ** 2 * 2:
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while not (pass_flag[0] and pass_flag[1]) and num < size[game_name] ** 2 * 2:
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turn = num % 2
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board = player[turn].run_cmd(str(num) + ' show_board')
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board = eval(board[board.index('['):board.index(']') + 1])
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for i in range(size["reversi"]):
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for j in range(size["reversi"]):
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print show[board[i * size["reversi"] + j]] + " ",
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for i in range(size[game_name]):
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for j in range(size[game_name]):
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print show[board[i * size[game_name] + j]] + " ",
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print "\n",
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data.boards.append(board)
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start_time = time.time()
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@ -26,6 +26,7 @@ if __name__ == '__main__':
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parser.add_argument("--checkpoint_path", type=str, default=None)
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parser.add_argument("--role", type=str, default="unknown")
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parser.add_argument("--debug", type=str, default=False)
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parser.add_argument("--game", type=str, default=False)
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args = parser.parse_args()
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if args.checkpoint_path == 'None':
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@ -33,7 +34,7 @@ if __name__ == '__main__':
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debug = False
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if args.debug == "True":
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debug = True
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game = Game(role=args.role, checkpoint_path=args.checkpoint_path, debug=debug)
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game = Game(name=args.game, role=args.role, checkpoint_path=args.checkpoint_path, debug=debug)
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engine = GTPEngine(game_obj=game, name='tianshou', version=0)
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daemon = Pyro4.Daemon() # make a Pyro daemon
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@ -1,123 +0,0 @@
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import os
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import numpy as np
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import time
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size = 9
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path = "/raid/tongzheng/tianshou/AlphaGo/data/part1/"
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save_path = "/raid/tongzheng/tianshou/AlphaGo/data/"
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name = os.listdir(path)
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print(len(name))
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batch_size = 128
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batch_num = 512
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block_size = batch_size * batch_num
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slots_num = 16
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class block(object):
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def __init__(self, block_size, block_id):
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self.boards = []
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self.wins = []
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self.ps = []
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self.block_size = block_size
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self.block_id = block_id
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def concat(self, board, p, win):
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board = board.reshape(-1, size, size, 17)
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win = win.reshape(-1, 1)
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p = p.reshape(-1, size ** 2 + 1)
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self.boards.append(board)
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self.wins.append(win)
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self.ps.append(p)
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def isfull(self):
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assert len(self.boards) == len(self.wins)
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assert len(self.boards) == len(self.ps)
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return len(self.boards) == self.block_size
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def save_and_reset(self, block_id):
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self.boards = np.concatenate(self.boards, axis=0)
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self.wins = np.concatenate(self.wins, axis=0)
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self.ps = np.concatenate(self.ps, axis=0)
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print ("Block {}, Boards shape {}, Wins Shape {}, Ps Shape {}".format(self.block_id, self.boards.shape[0],
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self.wins.shape[0], self.ps.shape[0]))
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np.savez(save_path + "block" + str(self.block_id), boards=self.boards, wins=self.wins, ps=self.ps)
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self.boards = []
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self.wins = []
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self.ps = []
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self.block_id = block_id
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def store_num(self):
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assert len(self.boards) == len(self.wins)
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assert len(self.boards) == len(self.ps)
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return len(self.boards)
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def concat(block_list, board, win, p):
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global index
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seed = np.random.randint(slots_num)
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block_list[seed].concat(board, win, p)
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if block_list[seed].isfull():
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block_list[seed].save_and_reset(index)
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index = index + 1
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block_list = []
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for index in range(slots_num):
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block_list.append(block(block_size, index))
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index = index + 1
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for n in name:
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data = np.load(path + n)
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board = data["boards"]
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win = data["win"]
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p = data["p"]
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print("Start {}".format(n))
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print("Shape {}".format(board.shape[0]))
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start = -time.time()
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for i in range(board.shape[0]):
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board_ori = board[i].reshape(-1, size, size, 17)
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win_ori = win[i].reshape(-1, 1)
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p_ori = p[i].reshape(-1, size ** 2 + 1)
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concat(block_list, board_ori, p_ori, win_ori)
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for t in range(1, 4):
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board_aug = np.rot90(board_ori, t, (1, 2))
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p_aug = np.concatenate(
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[np.rot90(p_ori[:, :-1].reshape(-1, size, size), t, (1, 2)).reshape(-1, size ** 2), p_ori[:, -1].reshape(-1, 1)],
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axis=1)
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concat(block_list, board_aug, p_aug, win_ori)
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board_aug = board_ori[:, ::-1]
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p_aug = np.concatenate(
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[p_ori[:, :-1].reshape(-1, size, size)[:, ::-1].reshape(-1, size ** 2), p_ori[:, -1].reshape(-1, 1)],
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axis=1)
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concat(block_list, board_aug, p_aug, win_ori)
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board_aug = board_ori[:, :, ::-1]
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p_aug = np.concatenate(
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[p_ori[:, :-1].reshape(-1, size, size)[:, :, ::-1].reshape(-1, size ** 2), p_ori[:, -1].reshape(-1, 1)],
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axis=1)
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concat(block_list, board_aug, p_aug, win_ori)
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board_aug = np.rot90(board_ori[:, ::-1], 1, (1, 2))
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p_aug = np.concatenate(
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[np.rot90(p_ori[:, :-1].reshape(-1, size, size)[:, ::-1], 1, (1, 2)).reshape(-1, size ** 2),
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p_ori[:, -1].reshape(-1, 1)],
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axis=1)
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concat(block_list, board_aug, p_aug, win_ori)
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board_aug = np.rot90(board_ori[:, :, ::-1], 1, (1, 2))
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p_aug = np.concatenate(
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[np.rot90(p_ori[:, :-1].reshape(-1, size, size)[:, :, ::-1], 1, (1, 2)).reshape(-1, size ** 2),
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p_ori[:, -1].reshape(-1, 1)],
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axis=1)
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concat(block_list, board_aug, p_aug, win_ori)
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print ("Finished {} with time {}".format(n, time.time() + start))
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data_num = 0
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for i in range(slots_num):
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print("Block {} ".format(block_list[i].block_id) + "Size {}".format(block_list[i].store_num()))
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data_num = data_num + block_list[i].store_num()
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print ("Total data {}".format(data_num))
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for i in range(slots_num):
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block_list[i].save_and_reset(block_list[i].block_id)
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@ -1,103 +0,0 @@
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from game import Game
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from engine import GTPEngine
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import re
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import numpy as np
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import os
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from collections import deque
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import utils
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument('--result_path', type=str, default='./part1')
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args = parser.parse_args()
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if not os.path.exists(args.result_path):
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os.makedirs(args.result_path)
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game = Game()
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engine = GTPEngine(game_obj=game)
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history = deque(maxlen=8)
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for i in range(8):
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history.append(game.board)
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state = []
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prob = []
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winner = []
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pattern = "[A-Z]{1}[0-9]{1}"
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game.show_board()
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def history2state(history, color):
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state = np.zeros([1, game.size, game.size, 17])
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for i in range(8):
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state[0, :, :, i] = np.array(np.array(history[i]) == np.ones(game.size ** 2)).reshape(game.size, game.size)
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state[0, :, :, i + 8] = np.array(np.array(history[i]) == -np.ones(game.size ** 2)).reshape(game.size, game.size)
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if color == utils.BLACK:
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state[0, :, :, 16] = np.ones([game.size, game.size])
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if color == utils.WHITE:
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state[0, :, :, 16] = np.zeros([game.size, game.size])
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return state
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num = 0
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game_num = 0
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black_pass = False
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white_pass = False
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while True:
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print("Start game {}".format(game_num))
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while not (black_pass and white_pass) and num < game.size ** 2 * 2:
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if num % 2 == 0:
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color = utils.BLACK
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new_state = history2state(history, color)
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state.append(new_state)
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result = engine.run_cmd(str(num) + " genmove BLACK")
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num += 1
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match = re.search(pattern, result)
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if match is not None:
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print(match.group())
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else:
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print("pass")
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if re.search("pass", result) is not None:
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black_pass = True
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else:
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black_pass = False
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else:
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color = utils.WHITE
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new_state = history2state(history, color)
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state.append(new_state)
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result = engine.run_cmd(str(num) + " genmove WHITE")
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num += 1
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match = re.search(pattern, result)
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if match is not None:
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print(match.group())
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else:
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print("pass")
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if re.search("pass", result) is not None:
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white_pass = True
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else:
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white_pass = False
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game.show_board()
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prob.append(np.array(game.prob).reshape(-1, game.size ** 2 + 1))
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print("Finished")
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print("\n")
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score = game.game_engine.executor_get_score(game.board)
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if score > 0:
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winner = utils.BLACK
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else:
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winner = utils.WHITE
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state = np.concatenate(state, axis=0)
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prob = np.concatenate(prob, axis=0)
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winner = np.ones([num, 1]) * winner
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assert state.shape[0] == prob.shape[0]
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assert state.shape[0] == winner.shape[0]
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np.savez(args.result_path + "/game" + str(game_num), state=state, prob=prob, winner=winner)
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state = []
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prob = []
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winner = []
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num = 0
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black_pass = False
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white_pass = False
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engine.run_cmd(str(num) + " clear_board")
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history.clear()
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for _ in range(8):
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history.append(game.board)
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game_num += 1
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@ -40,28 +40,27 @@ class MCTSNode(object):
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class UCTNode(MCTSNode):
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def __init__(self, parent, action, state, action_num, prior, debug=False, inverse=False):
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def __init__(self, parent, action, state, action_num, prior, mcts, inverse=False):
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super(UCTNode, self).__init__(parent, action, state, action_num, prior, inverse)
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self.Q = np.zeros([action_num])
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self.W = np.zeros([action_num])
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self.N = np.zeros([action_num])
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self.ucb = self.Q + c_puct * self.prior * math.sqrt(np.sum(self.N)) / (self.N + 1)
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self.mask = None
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self.debug=debug
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self.elapse_time = 0
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def clear_elapse_time(self):
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self.elapse_time = 0
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self.mcts = mcts
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def selection(self, simulator):
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head = time.time()
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self.valid_mask(simulator)
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self.elapse_time += time.time() - head
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self.mcts.valid_mask_time += time.time() - head
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action = np.argmax(self.ucb)
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if action in self.children.keys():
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self.mcts.state_selection_time += time.time() - head
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return self.children[action].selection(simulator)
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else:
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self.children[action] = ActionNode(self, action)
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self.children[action] = ActionNode(self, action, mcts=self.mcts)
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self.mcts.state_selection_time += time.time() - head
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return self.children[action].selection(simulator)
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def backpropagation(self, action):
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@ -100,7 +99,7 @@ class TSNode(MCTSNode):
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class ActionNode(object):
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def __init__(self, parent, action):
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def __init__(self, parent, action, mcts):
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self.parent = parent
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self.action = action
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self.children = {}
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@ -108,12 +107,18 @@ class ActionNode(object):
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self.origin_state = None
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self.state_type = None
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self.reward = 0
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self.mcts = mcts
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def type_conversion_to_tuple(self):
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t0 = time.time()
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if isinstance(self.next_state, np.ndarray):
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self.next_state = self.next_state.tolist()
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t1 = time.time()
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if isinstance(self.next_state, list):
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self.next_state = list2tuple(self.next_state)
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t2 = time.time()
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self.mcts.ndarray2list_time += t1 - t0
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self.mcts.list2tuple_time += t2 - t1
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def type_conversion_to_origin(self):
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if isinstance(self.state_type, np.ndarray):
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@ -122,23 +127,28 @@ class ActionNode(object):
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self.next_state = tuple2list(self.next_state)
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def selection(self, simulator):
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head = time.time()
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self.next_state, self.reward = simulator.simulate_step_forward(self.parent.state, self.action)
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self.mcts.simulate_sf_time += time.time() - head
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self.origin_state = self.next_state
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self.state_type = type(self.next_state)
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self.type_conversion_to_tuple()
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if self.next_state is not None:
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if self.next_state in self.children.keys():
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self.mcts.action_selection_time += time.time() - head
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return self.children[self.next_state].selection(simulator)
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else:
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self.mcts.action_selection_time += time.time() - head
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return self.parent, self.action
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else:
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self.mcts.action_selection_time += time.time() - head
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return self.parent, self.action
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def expansion(self, evaluator, action_num):
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if self.next_state is not None:
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prior, value = evaluator(self.next_state)
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self.children[self.next_state] = UCTNode(self, self.action, self.origin_state, action_num, prior,
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self.parent.inverse)
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mcts=self.mcts, inverse=self.parent.inverse)
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return value
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else:
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return 0.
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@ -160,11 +170,23 @@ class MCTS(object):
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if method == "":
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self.root = root
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if method == "UCT":
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self.root = UCTNode(None, None, root, action_num, prior, self.debug, inverse=inverse)
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self.root = UCTNode(None, None, root, action_num, prior, mcts=self, inverse=inverse)
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if method == "TS":
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self.root = TSNode(None, None, root, action_num, prior, inverse=inverse)
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self.inverse = inverse
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# time spend on each step
|
||||
self.selection_time = 0
|
||||
self.expansion_time = 0
|
||||
self.backpropagation_time = 0
|
||||
self.action_selection_time = 0
|
||||
self.state_selection_time = 0
|
||||
self.simulate_sf_time = 0
|
||||
self.valid_mask_time = 0
|
||||
self.ndarray2list_time = 0
|
||||
self.list2tuple_time = 0
|
||||
self.check = 0
|
||||
|
||||
def search(self, max_step=None, max_time=None):
|
||||
step = 0
|
||||
start_time = time.time()
|
||||
@ -175,23 +197,25 @@ class MCTS(object):
|
||||
if max_step is None and max_time is None:
|
||||
raise ValueError("Need a stop criteria!")
|
||||
|
||||
selection_time = 0
|
||||
expansion_time = 0
|
||||
backprop_time = 0
|
||||
self.root.clear_elapse_time()
|
||||
while step < max_step and time.time() - start_time < max_step:
|
||||
sel_time, exp_time, back_time = self._expand()
|
||||
selection_time += sel_time
|
||||
expansion_time += exp_time
|
||||
backprop_time += back_time
|
||||
self.selection_time += sel_time
|
||||
self.expansion_time += exp_time
|
||||
self.backpropagation_time += back_time
|
||||
step += 1
|
||||
if (self.debug):
|
||||
file = open("debug.txt", "a")
|
||||
file = open("mcts_profiling.txt", "a")
|
||||
file.write("[" + str(self.role) + "]"
|
||||
+ " selection : " + str(selection_time) + "\t"
|
||||
+ " validmask : " + str(self.root.elapse_time) + "\t"
|
||||
+ " expansion : " + str(expansion_time) + "\t"
|
||||
+ " backprop : " + str(backprop_time) + "\t"
|
||||
+ " sel " + '%.3f' % self.selection_time + " "
|
||||
+ " sel_sta " + '%.3f' % self.state_selection_time + " "
|
||||
+ " valid " + '%.3f' % self.valid_mask_time + " "
|
||||
+ " sel_act " + '%.3f' % self.action_selection_time + " "
|
||||
+ " array2list " + '%.4f' % self.ndarray2list_time + " "
|
||||
+ " check " + str(self.check) + " "
|
||||
+ " list2tuple " + '%.4f' % self.list2tuple_time + " \t"
|
||||
+ " forward " + '%.3f' % self.simulate_sf_time + " "
|
||||
+ " exp " + '%.3f' % self.expansion_time + " "
|
||||
+ " bak " + '%.3f' % self.backpropagation_time + " "
|
||||
+ "\n")
|
||||
file.close()
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user