def randomGame():
players = []
player1 = player1class.myPlayer()
player1.newGame(Goban.Board._BLACK)
players.append(player1)
player2class = importlib.import_module(classNames[1])
player2 = player2class.myPlayer()
player2.newGame(Goban.Board._WHITE)
players.append(player2)
json_data = {}
b = Goban.Board()
nextplayer = 0
nextplayercolor = Goban.Board._BLACK
nbmoves = 1
wrongmovefrom = 0
json_data["list_of_moves"] = playgame(b, nextplayer, nextplayercolor,
nbmoves, wrongmovefrom, players)
result = b.result()
if wrongmovefrom > 0:
if wrongmovefrom == b._WHITE:
json_data["winner"] = "black"
elif wrongmovefrom == b._BLACK:
json_data["winner"] = "white"
else:
print("ERROR")
elif result == "1-0":
json_data["winner"] = "white"
elif result == "0-1":
json_data["winner"] = "black"
else:
return
with open('data.json', 'a') as f:
f.write(json.JSONEncoder().encode(json_data))
f.write(',')
def __init__(self):
self._board = Goban.Board()
self._mycolor = None
self._behavior = AlphaBeta()
self._nbCoup = 0
self._movePossible1 = {"B2", "C3", "G3", "H2", "A5", "J5", "B8", "H8", "D9", "F8"}
self._movePossible2 = {"D2", "F2", "B5", "H5", "C7", "G7", "E8", "A4", "J4"}
self._time = 0
def __init__(self, model_champion1, model_champion2):
self.b = Goban.Board()
self.nextplayercolor = Goban.Board._BLACK
self.nbmoves = 0
self.list_moves_colors = [[], []]
self.list_of_moves = []
self.player = []
self.initialize_player(model_champion1, model_champion2)
def __init__(self):
self._board = Goban.Board()
self._mycolor = None
self._model = tf.keras.models.load_model(
'myplayer_model.h5', custom_objects={'my_metric': my_metric})
self._last_opponent_move = None
self._last_move = None
self._turn = 0
self._timer = 0
def __init__(self):
self._board = Goban.Board()
self._mycolor = None
self._black_goban = [] #Liste des coups réellement joué par Noir
self._white_goban = [] #Liste des coups réellement joué par Blanc
self._count = 0 #Numéro du tour
#Utilisé dans l'iterative-deepening
self._last_best_move = 0
self._start = 0
self._end = 0
def __init__(self):
self._mycolor = None
self.model = AlphaGoZero()
self.device = "cpu"
self.model.eval()
self.max_depth: int = 100
self.simulations_per_play: int = 100
# Number of boards to keep for history
self.len_history: int = 7
self.tensor_size: int = 2 * self.len_history + 1
self.board: Goban.Board = Goban.Board()
self.np_array: np.ndarray = np.zeros((9, 9), dtype=np.float)
self.torch_board = torch.from_numpy(self.np_array)
def build_goban_from_moves(moves):
board = Goban.Board()
for i, move in enumerate(moves):
if move != None:
name = Goban.Board.flat_to_name(move)
coord = Goban.Board.name_to_coord(name)
# Vérifie que le push du move ne crée pas d'erreur (ex: jouer dans un oeil)
try:
board.push(Goban.Board.flatten(coord))
except KeyError:
return False, None
return True, board
def play_a_game():
player1 = gnugoPlayer.myPlayer()
player2 = gnugoPlayer.myPlayer()
player1.newGame(Goban.Board._BLACK)
player2.newGame(Goban.Board._WHITE)
players = [player1, player2]
b = Goban.Board()
nextplayer = 0
nextplayercolor = Goban.Board._BLACK
states = list()
actions = list()
while not b.is_game_over():
# save the board as state
states.append(np.copy(b._board))
legals = b.legal_moves()
otherplayer = (nextplayer + 1) % 2
othercolor = Goban.Board.flip(nextplayercolor)
move = players[nextplayer].getPlayerMove()
# save the move as chosen action
actions.append(Goban.Board.name_to_flat(move))
if not Goban.Board.name_to_flat(move) in legals:
# illegal move
return None, None, None
b.push(Goban.Board.name_to_flat(move))
players[otherplayer].playOpponentMove(move)
nextplayer = otherplayer
nextplayercolor = othercolor
[p.endGame(None) for p in players]
result = b.result()
if result == "1-0": winner = 1
elif result == "0-1": winner = 0
else: winner = -1
# give reward
rewards = [(-1)**(n + winner) for n in range(len(actions))
] if winner != -1 else [0] * len(actions)
return states, actions, rewards
def __init__(self, model, device):
self.model = model
self.device = device
self.model.eval()
# Max Game duration
self.T: int = 200
self.max_depth: int = 100
self.v_resign: float = -0.99
self.simulations_per_play: int = 100
# Number of moves after which temperature is set to something close to 0
self.moves_after_low_temperature: int = 7 # defined empirically/proportionally
# Number of boards to keep for history
self.len_history: int = 7
self.tensor_size: int = 2 * self.len_history + 1
self.logger: logging.Logger = logging.getLogger("MCTS")
self.board: Goban.Board = Goban.Board()
self.np_array: np.ndarray = np.zeros((9, 9), dtype=np.float)
self.torch_board = torch.from_numpy(self.np_array)
if VISUALIZE:
self.gnugo = GnuGo(9)
self.moves = self.gnugo.Moves(self.gnugo)
def encoder(data, h=5, liberties=0):
global rejected
board = Goban.Board()
moves = data["list_of_moves"]
if len(moves) < h:
return None
b = np.zeros((9, 9, h * (2 + liberties) + 1))
for i in range(len(moves)):
try:
board.push(board.flatten(board.name_to_coord(moves[i])))
except Exception as var:
rejected += 1
return None
if i >= len(moves) - h:
tmp = board_encoding(board, liberties)
b[:, :, (2 + liberties) * (i - len(moves) + h):(2 + liberties) *
(i - len(moves) + h + 1)] = tmp[:, :, :2 + liberties]
if len(moves) % 2 == 1:
b[:, :, -1] = 1
#boards = board_encoding(board)
proba_move = np.array(data["proba_next_move"][:-1]).reshape((9, 9))
proba_pass = data["proba_next_move"][-1]
proba_win = 2 * data["proba_win"] - 1
return b, proba_move, proba_pass, proba_win
def __init__(self):
self._board = Goban.Board()
self._mycolor = None
from io import StringIO
import sys
def fileorpackage(name):
if name.endswith(".py"):
return name[:-3]
return name
if len(sys.argv) > 2:
classNames = [fileorpackage(sys.argv[1]), fileorpackage(sys.argv[2])]
elif len(sys.argv) > 1:
classNames = [fileorpackage(sys.argv[1]), 'myPlayer']
else:
classNames = ['myPlayer', 'myPlayer']
b = Goban.Board()
players = []
player1class = importlib.import_module(classNames[0])
player1 = player1class.myPlayer()
player1.newGame(Goban.Board._BLACK)
players.append(player1)
player2class = importlib.import_module(classNames[1])
player2 = player2class.myPlayer()
player2.newGame(Goban.Board._WHITE)
players.append(player2)
totalTime = [0,0] # total real time for each player
nextplayer = 0
nextplayercolor = Goban.Board._BLACK
def __init__(self):
self._board = Goban.Board()
self._mycolor = None
self.tree = MCTS_TREE(self._board)
reward = table['black_wins'] / table['rollouts']
else: # white plays next
reward = table['white_wins'] / table['rollouts']
gnugo.query("clear_board")
return probs, reward
history_size = 7
samples = []
gnugo = GnuGo(9)
tables = get_raw_data_go()
board = Goban.Board()
count = 0
for idx, table in enumerate(tqdm(tables)):
assert table['depth'] == len(table['list_of_moves'])
vector = np.zeros((2 * history_size + 1, 9, 9), dtype=np.float64)
next_to_play = 0
skip = False
for move in table['list_of_moves'][:-history_size]:
try:
board.push(Goban.Board.name_to_flat(move))
except Exception:
skip = True
def reset(self):
"""
Reset and init the current game.
"""
self.board: Goban.Board = Goban.Board()
self._vector = torch.zeros((self.tensor_size, 9, 9), dtype=int)
def __init__(self):
self._board = Goban.Board()
self._mycolor = None
self._turn = 0
self._depth = 2
def __init__(self):
self._board = Goban.Board()
self._NNboard = NNboard(self._board)
self._mycolor = None
self._turn = 0
# Starting depth
self._depth = 2
# Importants steps in the game (for MinMAx and AlphaBeta strategies)
self._mid_game = 20
self._late_game = 40
self._very_late_game = 50
# Maximal time for a move (for Iterative Deepening)
self._timeout = 5
# T0 of a move
self._t0 = None
# List storing time taken by the player at each move
self._time_history = []
# Openings
self._opening_length = 5 # Number of opening moves the player will play
self._opening = None # Will contain an opening
self._opening_index = 0 # To keep track of where to start checking opening moves
self._openings = []
# Model
self._model = None
if os.path.exists('games.json'):
with open('games.json') as json_file:
data = json.load(json_file)
for g in data:
self._openings.append(g['moves'])
else:
self._openings = [
[
"C6", "F4", "D4", "F6", "H5", "D3", "C3", "D5", "C4", "E4",
"G7", "F7", "G8", "H4", "G5", "G4", "D6", "F8", "G6", "D2",
"C2", "D8", "C8", "E5", "J4", "J3", "J5", "C5", "B5", "B7",
"B6", "C7", "H3", "A6", "D7", "B8", "E8", "C9", "F2", "G3",
"H2", "G2", "F9", "E2", "E9", "E7", "G9", "D9", "F3", "H1",
"J2", "C1", "D1", "B1", "B2", "E1", "A4", "A7"
],
[
"E5", "E7", "E3", "E2", "D2", "G6", "D7", "D8", "D6", "C8",
"G5", "H5", "H6", "G4", "G7", "F5", "F6", "E4", "G5", "F3",
"D3", "G6", "E8", "D4", "C4", "D5", "E6", "C5", "B4", "G5",
"F2", "G2", "C6", "B5", "B6", "A6", "A7", "A5", "B8", "B2",
"C2", "F1"
],
[
"E5", "E7", "E3", "E2", "D2", "G6", "G5", "H5", "D7", "D8",
"C7", "E6", "F5", "D6", "F6", "G7", "H4", "C6", "B5", "D3",
"D4", "C3", "C2", "C4", "C5", "D5", "E4", "B6", "B3", "A5",
"B4", "H6", "F7", "F8", "J4", "J5", "A4", "A6"
],
[
"E5", "E7", "E3", "E2", "D2", "F3", "E4", "G5", "G4", "G3",
"D7", "D8", "C7", "C8", "F7", "E6", "F6", "F8", "D6", "E8",
"B8", "H4", "F5", "H7", "H5", "F4", "H6", "H8", "G6", "E1",
"H2", "C2", "D3", "D1", "G2", "F2", "H3", "J4", "C3", "G4",
"B2", "G1", "H1", "J2", "C1", "J3", "F1", "B7", "B6", "G1",
"G7", "G8", "F1", "B9", "J5", "G1", "C9", "J1", "A9", "D9",
"J7", "B9", "A7", "A8", "B7", "J8", "A9", "C9", "A8", "J6",
"PASS", "J7"
],
[
"E5", "G5", "G4", "C6", "C5", "D5", "D4", "D6", "C4", "H4",
"F4", "E6", "H5", "F5", "H6", "E4", "E3", "H3", "G7", "G2",
"E8", "D8", "E7", "E2", "D2", "F2", "D7", "C7", "E5", "B5",
"B4", "E4", "F3", "B8", "B6", "B7", "A5", "E9", "H8", "F9",
"G8", "D1", "E1", "F1", "C2", "J4", "C9", "D3", "E5", "D9",
"B9", "E4", "C3", "G3", "E5", "A7", "A9", "E4", "D3", "A6",
"B5", "B2", "B1", "A3", "C1", "A1", "E5"
],
[
"E4", "D6", "E6", "E7", "F6", "D4", "D3", "D5", "E3", "F7",
"G7", "G8", "D7", "G6", "H7", "D8", "C7", "C8", "B6", "B7",
"C6", "B5", "A6", "B8", "B4", "C4", "B3", "G5", "F4", "G4",
"G3", "H3", "H2", "G2", "F2", "F3", "F8", "E8", "G3", "A5"
],
[
"G4", "D5", "D3", "E7", "G7", "F5", "G5", "E3", "D4", "E4",
"C5", "C6", "D6", "E5", "B6", "C7", "B4", "D2", "B7", "G8",
"H8", "F7", "H6", "C3", "C4", "C8", "C2", "F2", "D1", "E1",
"E2", "H7", "J7", "D2", "G2", "G3", "H3", "B2", "E2", "H2",
"F1", "G1", "F8", "E1", "D2", "E8", "G9", "H4", "B8", "B9",
"H5", "J3", "F4", "F3", "J5", "E9", "F9", "J4", "F6", "E6",
"A8", "F1", "G6"
],
[
"G4", "C6", "E7", "E5", "E3", "G5", "G7", "F4", "F3", "H7",
"H4", "G6", "C7", "D6", "D7", "B7", "B8", "B5", "A7", "A6",
"B6", "G2", "G3", "B7", "F6", "F5", "B6", "D2", "A5", "A4",
"D3", "C2", "C3", "B3", "C5", "A6", "D5", "E6", "A5"
],
[
"E5", "E7", "F7", "F3", "E6", "D7", "F8", "C6", "C5", "B5",
"B4", "C4", "D5", "B6", "C3", "G5", "C8", "D4", "E3", "B3",
"E4", "A4", "G3", "G6", "H7", "G7", "G8", "H8", "H9", "G2",
"G4", "F4", "F2", "F5", "H2", "E2", "G1", "D3", "D2", "C2",
"E8", "D1"
],
[
"G4", "D5", "D3", "G6", "D7", "F7", "B7", "C3", "C2", "D2",
"E2", "D4", "E3", "B2", "D1", "B4", "E8", "H4", "H5", "G5",
"H3", "F4", "G3", "F8", "F9", "B6", "G8", "B8", "H6", "G7",
"H7", "H8", "G9", "C7", "C8", "A7", "C6", "D6", "B7", "H9",
"E9", "C7", "E7", "E6", "B7"
],
[
"C5", "G5", "F5", "G6", "F4", "G4", "F6", "F7", "E7", "F3",
"G7", "F8", "E8", "E3", "H7", "D5", "D4", "C4", "D6", "D3",
"E5", "B4", "H6", "H5"
],
[
"E5", "E7", "E3", "E2", "F2", "C6", "C5", "B5", "D6", "C4",
"D5", "C7", "D7", "F3", "F4", "G3", "D2", "G2", "E1", "D8",
"H5", "F8", "B3", "B4", "G7", "G8", "H8", "F6", "G6", "H9",
"J8", "C3", "B2", "C2", "C1", "G4", "F5", "G5", "F7", "H4",
"J6", "E8", "E6", "H2", "J3", "J4", "G1", "H1", "J2", "F1",
"C8", "E2", "B8", "B6", "F2", "B1", "E2", "D1", "D9", "E4",
"D4", "D3", "C1", "H6", "A1", "J5", "H7", "J7", "E9", "C9",
"B9", "G9", "C9"
],
[
"E5", "E3", "F3", "F4", "E4", "G3", "F2", "D3", "C4", "G2",
"G4", "F5", "F6", "G5", "G6", "H4", "H5", "G4", "C6", "C3",
"B4", "C7", "B7", "D7", "B8", "H7", "H6", "F8", "H8", "G8",
"G7", "H9", "J8", "E6", "E7", "D6", "D5", "D8", "F7", "E8",
"G9", "J5", "J7", "J4", "J6", "B6", "C5", "C8", "C9", "B9",
"A9", "D9", "F9", "B9", "A6", "A8", "B5", "B3", "A4", "E9",
"G9"
],
[
"E6", "D4", "F4", "C6", "D3", "G7", "C4", "D5", "E3", "C3",
"B3", "F5", "F6", "C2", "B5", "B6", "B2", "E4", "G4", "F3",
"G3", "F2", "D2", "G5", "G6", "H5", "H6", "G2", "D7", "J6",
"J7", "J5", "H7", "C8"
],
[
"E6", "E4", "D4", "D3", "D5", "C3", "F4", "F3", "G4", "G3",
"E7", "B5", "B4", "C4", "B6", "C5", "C6", "E5", "F5", "D6",
"D7", "H4", "H5", "F6", "G5", "H6"
],
[
"E5", "E7", "C6", "E3", "G4", "G6", "F7", "F6", "E6", "F8",
"D7", "G7", "C3", "C2", "B2", "D3", "C4", "F4", "G2", "F5",
"H5", "D8", "C8", "C9", "B9", "D9", "B8", "F2", "G3", "H6",
"C1", "D2", "J2", "B1", "A1", "D1", "B3", "B1", "A2", "G1",
"H1", "F1", "J6", "J7", "J5", "G5", "H4", "D4", "D5", "C1"
],
[
"G4", "D5", "D3", "F6", "C7", "C5", "F8", "C3", "F3", "D2",
"E2", "D4", "E3", "G5", "E7", "G8", "B6", "F7", "E8", "H5",
"H4", "J4", "J3", "J5", "H2", "B5", "G9", "H8", "C2", "B2",
"D1", "B8", "B7", "C8", "A8", "B1", "B3", "C4", "H9", "F9",
"E9", "A6", "D8", "B9", "H7", "G7", "E6", "D6", "E5", "E4",
"F5", "H6", "D7", "F4", "A2"
]
]
# Retrieve learnt model saved on disk (for heuristic)
# load json and create model
json_file = open('./model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
self._model = tf.keras.models.model_from_json(loaded_model_json)
# load weights into new model
self._model.load_weights("./model.h5")
# evaluate loaded model on test data
self._model.compile(loss='mae', optimizer='adam', metrics=['accuracy'])
def __init__(self):
self._board = Goban.Board()
self._mycolor = None
self.nbmoves = 0
self.total_time = 0
def __init__(self):
self._board = Goban.Board()
self._gnugo = GnuGo.GnuGo(Goban.Board._BOARDSIZE)
self._moves = self._gnugo.Moves(self._gnugo)
self._mycolor = None