def get_buffer(config, game) -> (ChessEnv, list):
"""
Gets data to load into the buffer by playing a game using PGN data.
:param Config config: config to use to play the game
:param pgn.Game game: game to play
:return list(str,list(float)): data from this game for the SupervisedLearningWorker.buffer
"""
env = ChessEnv().reset()
white = ChessPlayer(config, dummy=True)
black = ChessPlayer(config, dummy=True)
result = game.headers["Result"]
white_elo, black_elo = int(game.headers["WhiteElo"]), int(
game.headers["BlackElo"])
white_weight = clip_elo_policy(config, white_elo)
black_weight = clip_elo_policy(config, black_elo)
actions = []
while not game.is_end():
game = game.variation(0)
actions.append(game.move.uci())
k = 0
while not env.done and k < len(actions):
if env.white_to_move:
action = white.sl_action(env.observation,
actions[k],
weight=white_weight) #ignore=True
else:
action = black.sl_action(env.observation,
actions[k],
weight=black_weight) #ignore=True
env.step(action, False)
k += 1
if not env.board.is_game_over() and result != '1/2-1/2':
env.resigned = True
if result == '1-0':
env.winner = Winner.white
black_win = -1
elif result == '0-1':
env.winner = Winner.black
black_win = 1
else:
env.winner = Winner.draw
black_win = 0
black.finish_game(black_win)
white.finish_game(-black_win)
data = []
for i in range(len(white.moves)):
data.append(white.moves[i])
if i < len(black.moves):
data.append(black.moves[i])
return env, data
def get_buffer(config, game) -> (ChessEnv, list):
"""
Gets data to load into the buffer by playing a game using PGN data.
:param Config config: config to use to play the game
:param pgn.Game game: game to play
:return list(str,list(float)): data from this game for the SupervisedLearningWorker.buffer
"""
env = ChessEnv().reset()
white = ChessPlayer(config, dummy=True)
black = ChessPlayer(config, dummy=True)
result = game.headers["Result"]
white_elo, black_elo = int(game.headers["WhiteElo"]), int(game.headers["BlackElo"])
white_weight = clip_elo_policy(config, white_elo)
black_weight = clip_elo_policy(config, black_elo)
actions = []
while not game.is_end():
game = game.variation(0)
actions.append(game.move.uci())
k = 0
while not env.done and k < len(actions):
if env.white_to_move:
action = white.sl_action(env.observation, actions[k], weight=white_weight) #ignore=True
else:
action = black.sl_action(env.observation, actions[k], weight=black_weight) #ignore=True
env.step(action, False)
k += 1
if not env.board.is_game_over() and result != '1/2-1/2':
env.resigned = True
if result == '1-0':
env.winner = Winner.white
black_win = -1
elif result == '0-1':
env.winner = Winner.black
black_win = 1
else:
env.winner = Winner.draw
black_win = 0
black.finish_game(black_win)
white.finish_game(-black_win)
data = []
for i in range(len(white.moves)):
data.append(white.moves[i])
if i < len(black.moves):
data.append(black.moves[i])
return env, data
def get_buffer(config, game) -> (ChessEnv, list):
env = ChessEnv().reset()
white = ChessPlayer(config, dummy=True)
black = ChessPlayer(config, dummy=True)
result = game.headers["Result"]
white_elo, black_elo = int(game.headers["WhiteElo"]), int(game.headers["BlackElo"])
white_weight = clip_elo_policy(config, white_elo)
black_weight = clip_elo_policy(config, black_elo)
actions = []
while not game.is_end():
game = game.variation(0)
actions.append(game.move.uci())
k = 0
while not env.done and k < len(actions):
if env.white_to_move:
action = white.sl_action(env.observation, actions[k], weight=white_weight) #ignore=True
else:
action = black.sl_action(env.observation, actions[k], weight=black_weight) #ignore=True
env.step(action, False)
k += 1
if not env.board.is_game_over() and result != '1/2-1/2':
env.resigned = True
if result == '1-0':
env.winner = Winner.white
black_win = -1
elif result == '0-1':
env.winner = Winner.black
black_win = 1
else:
env.winner = Winner.draw
black_win = 0
black.finish_game(black_win)
white.finish_game(-black_win)
data = []
for i in range(len(white.moves)):
data.append(white.moves[i])
if i < len(black.moves):
data.append(black.moves[i])
return env, data
def get_buffer(game, config) -> (ChessEnv, list):
env = ChessEnv().reset()
black = ChessPlayer(config, dummy=True)
white = ChessPlayer(config, dummy=True)
result = game.headers["Result"]
actions = []
while not game.is_end():
game = game.variation(0)
actions.append(game.move.uci())
k = 0
observation = env.observation
while not env.done and k < len(actions):
if env.board.turn == chess.WHITE:
action = white.sl_action(observation, actions[k]) #ignore=True
else:
action = black.sl_action(observation, actions[k]) #ignore=True
board, info = env.step(action, False)
observation = board.fen()
k += 1
env.done = True
if not env.board.is_game_over() and result != '1/2-1/2':
env.resigned = True
if result == '1-0':
env.winner = Winner.white
black_win = -1
elif result == '0-1':
env.winner = Winner.black
black_win = 1
else:
env.winner = Winner.draw
black_win = 0
black.finish_game(black_win)
white.finish_game(-black_win)
data = []
for i in range(len(white.moves)):
data.append(white.moves[i])
if i < len(black.moves):
data.append(black.moves[i])
return env, data
class SupervisedLearningWorker:
def __init__(self, config: Config, env=None, model=None):
"""
:param config:
:param ChessEnv|None env:
:param chess_zero.agent.model_chess.ChessModel|None model:
"""
self.config = config
self.model = model
self.env = env # type: ChessEnv
self.black = None # type: ChessPlayer
self.white = None # type: ChessPlayer
self.buffer = []
def start(self):
if self.model is None:
self.model = self.load_model()
self.buffer = []
idx = 1
while True:
start_time = time()
env = self.read_game(idx)
end_time = time()
logger.debug(f"game {idx} time={end_time - start_time} sec, "
f"turn={int(env.turn/2)}:{env.observation} - Winner:{env.winner} - by resignation?:{env.resigned}")
if (idx % self.config.play_data.nb_game_in_file) == 0:
reload_best_model_weight_if_changed(self.model)
idx += 1
def read_game(self, idx):
self.env.reset()
self.black = ChessPlayer(self.config, self.model)
self.white = ChessPlayer(self.config, self.model)
files = find_pgn_files(self.config.resource.play_data_dir)
if len(files) > 0:
random.shuffle(files)
filename = files[0]
pgn = open(filename, errors='ignore')
size = os.path.getsize(filename)
pos = random.randint(0, size)
pgn.seek(pos)
line = pgn.readline()
offset = 0
# Parse game headers.
while line:
if line.isspace() or line.startswith("%"):
line = pgn.readline()
continue
# Read header tags.
tag_match = TAG_REGEX.match(line)
if tag_match:
offset = pgn.tell()
break
line = pgn.readline()
pgn.seek(offset)
game = chess.pgn.read_game(pgn)
node = game
result = game.headers["Result"]
actions = []
while not node.is_end():
next_node = node.variation(0)
actions.append(node.board().uci(next_node.move))
node = next_node
pgn.close()
k = 0
observation = self.env.observation
while not self.env.done and k < len(actions):
if self.env.board.turn == chess.BLACK:
action = self.black.sl_action(observation, actions[k])
else:
action = self.white.sl_action(observation, actions[k])
board, info = self.env.step(action)
observation = board.fen()
k += 1
self.env.done = True
if not self.env.board.is_game_over() and result != '1/2-1/2':
self.env.resigned = True
if result == '1-0':
self.env.winner = Winner.white
elif result == '0-1':
self.env.winner = Winner.black
else:
self.env.winner = Winner.draw
self.finish_game()
self.save_play_data(write=idx % self.config.play_data.nb_game_in_file == 0)
self.remove_play_data()
return self.env
def save_play_data(self, write=True):
data = self.black.moves + self.white.moves
self.buffer += data
if not write:
return
rc = self.config.resource
game_id = datetime.now().strftime("%Y%m%d-%H%M%S.%f")
path = os.path.join(rc.play_data_dir, rc.play_data_filename_tmpl % game_id)
logger.info(f"save play data to {path}")
write_game_data_to_file(path, self.buffer)
self.buffer = []
def remove_play_data(self):
files = get_game_data_filenames(self.config.resource)
if len(files) < self.config.play_data.max_file_num:
return
for i in range(len(files) - self.config.play_data.max_file_num):
os.remove(files[i])
def finish_game(self):
if self.env.winner == Winner.black:
black_win = 1
elif self.env.winner == Winner.white:
black_win = -1
else:
black_win = 0
self.black.finish_game(black_win)
self.white.finish_game(-black_win)
def load_model(self):
from chess_zero.agent.model_chess import ChessModel
model = ChessModel(self.config)
if self.config.opts.new or not load_best_model_weight(model):
model.build()
save_as_best_model(model)
return model