def supervised_buffer(config, game) -> (ChessEnv, list): env = ChessEnv(config).reset() white = ChessPlayer(config, dummy=True) black = ChessPlayer(config, dummy=True) result = game.headers["Result"] env.board = game.board() for move in game.main_line(): ai = white if env.board.turn == chess.WHITE else black ai.sl_action(env, move) env.step(move) if not env.board.is_game_over() and result != '1/2-1/2': env.resigned = True if result == '1-0': env.winner = Winner.WHITE white_win = 1 elif result == '0-1': env.winner = Winner.BLACK white_win = -1 else: env.winner = Winner.DRAW white_win = 0 white.finish_game(white_win) black.finish_game(-white_win) return env, merge_data(white, black)
def self_play_buffer(config, cur) -> (ChessEnv, list): pipes = cur.pop() # borrow env = ChessEnv().reset() white = ChessPlayer(config, pipes=pipes) black = ChessPlayer(config, pipes=pipes) while not env.done: if env.white_to_move: action = white.action(env) else: action = black.action(env) env.step(action) if env.num_halfmoves >= config.play.max_game_length: env.adjudicate() if env.winner == Winner.white: black_win = -1 elif env.winner == Winner.black: black_win = 1 else: 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]) cur.append(pipes) 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): """ 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 self_play_buffer(config, cur) -> (GoBangEnv, list): """ Play one game and add the play data to the buffer :param Config config: config for how to play :param list(Connection) cur: list of pipes to use to get a pipe to send observations to for getting predictions. One will be removed from this list during the game, then added back :return (GoBangEnv,list((str,list(float)): a tuple containing the final GoBangEnv state and then a list of data to be appended to the SelfPlayWorker.buffer """ pipes = cur.pop() # borrow env = GoBangEnv().reset() white = ChessPlayer(config, pipes=pipes) black = ChessPlayer(config, pipes=pipes) while not env.done: if env.white_to_move: action = white.action(env) else: action = black.action(env) # pretty_print_panel(env.board.panel) # print(f'After action:{action}') env.step(action) # pretty_print_panel(env.board.panel) # print() # if env.num_halfmoves >= config.play.max_game_length: # env.adjudicate() if env.winner == Winner.white: black_score, white_score = -1, 1 elif env.winner == Winner.black: black_score, white_score = 1, -1 else: black_score, white_score = -0.5, -0.5 black.finish_game(black_score) white.finish_game(white_score) data = [] for i in range(len(black.moves)): data.append(black.moves[i]) if i < len(white.moves): data.append(white.moves[i]) pretty_print_panel(env.board.panel) print() #print(data) cur.append(pipes) 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
def self_play_buffer(config, cur) -> (ChessEnv, list): pipes = cur.pop() # borrow env = ChessEnv().reset() search_tree = defaultdict(VisitStats) white = ChessPlayer(config, search_tree=search_tree, pipes=pipes) black = ChessPlayer(config, search_tree=search_tree, pipes=pipes) history = [] cc = 0 while not env.done: if env.white_to_move: action = white.action(env) else: action = black.action(env) env.step(action) history.append(action) if len(history) > 6 and history[-1] == history[-5]: cc = cc + 1 else: cc = 0 if env.num_halfmoves >= config.play.max_game_length or cc >= 4: env.adjudicate() if env.winner == Winner.white: black_win = -1 elif env.winner == Winner.black: black_win = 1 else: 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]) cur.append(pipes) return env, data
class SelfPlayWorker: 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.start_game(idx) end_time = time() logger.debug(f"game {idx} time={end_time - start_time} sec, " f"turn={env.turn}:{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 start_game(self, idx): self.env.reset() self.black = ChessPlayer(self.config, self.model) self.white = ChessPlayer(self.config, self.model) observation = self.env.observation while not self.env.done: if self.env.board.turn == chess.BLACK: action = self.black.action(observation) else: action = self.white.action(observation) board, info = self.env.step(action) observation = board.fen() 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
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
def self_play_buffer(config, cur) -> (ChessEnv, list): """ Play one game and add the play data to the buffer :param Config config: config for how to play :param list(Connection) cur: list of pipes to use to get a pipe to send observations to for getting predictions. One will be removed from this list during the game, then added back :return (ChessEnv,list((str,list(float)): a tuple containing the final ChessEnv state and then a list of data to be appended to the SelfPlayWorker.buffer """ pipes = cur.pop() # borrow env = ChessEnv().reset() # EDIT CODE HERE TO CHANGE THE ENVIRONMENT white = ChessPlayer(config, pipes=pipes) black = ChessPlayer(config, pipes=pipes) move = 0 failed_play = 0 total_failed_plays = 0 print("Match Started") moves_list = "" while not env.done: # CHANGES_MADE_HERE temp = deepcopy(env) black_pieces = set("prnbqk") white_pieces = set("PRNBQK") if env.white_to_move: x = temp.board.piece_map() for i in x: if str(x[i]) in black_pieces: temp.board.remove_piece_at(i) action = white.action(temp) else: x = temp.board.piece_map() for i in x: if str(x[i]) in white_pieces: temp.board.remove_piece_at(i) action = black.action(temp) print("Match in Progress: ", move, "Moves made in the game, Failed Plays: ", total_failed_plays, end='\r') try: env.step(action) moves_list += action + ', ' failed_play = 0 move += 1 if env.num_halfmoves >= config.play.max_game_length: env.adjudicate() except ValueError: failed_play += 1 total_failed_plays += 1 if failed_play == 50: logger.warning("\nEnding the Game due to lack of development") env.adjudicate() continue # END_OF_CHANGES with open("result.csv", "a+") as fp: result = str(move) + ", " + str(total_failed_plays) + ", " + str( env.winner) + ", <" + env.board.fen() result += ">, Adjudicated\n" if failed_play == 50 else ">, Game End\n" fp.write(result) fp.close() with open("moves_list.csv", "a+") as fp: fp.write(moves_list) fp.write("\n") fp.close() if env.winner == Winner.white: black_win = -1 logger.info("White wins") elif env.winner == Winner.black: black_win = 1 logger.info("Black wins") else: black_win = 0 logger.info("Draw Match") 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]) cur.append(pipes) return env, data