def start(config: Config): chess_model = PlayWithHuman(config) while True: random_endgame = config.play.random_endgame if random_endgame == -1: env = ChessEnv(config).reset() else: env = ChessEnv(config).randomize(random_endgame) human_is_white = random() < 0.5 chess_model.start_game(human_is_white) print(env.board) while not env.done: if (env.board.turn == chess.WHITE) == human_is_white: action = chess_model.move_by_human(env) print(f"You move to: {env.board.san(action)}") else: action = chess_model.move_by_ai(env) print(f"AI moves to: {env.board.san(action)}") env.step(action) print(env.board) print(f"Board FEN = {env.fen}") game = chess.pgn.Game.from_board(env.board) game.headers['White'] = "Human" if human_is_white else f"AI {chess_model.model.digest[:10]}..." game.headers['Black'] = f"AI {chess_model.model.digest[:10]}..." if human_is_white else "Human" logger.debug("\n"+str(game)) print(f"\nEnd of the game. Game result: {env.board.result()}")
def play_game(self, current_model, ng_model, current_white: bool) -> (float, ChessEnv): env = ChessEnv().reset() current_player = ChessPlayer(self.config, model=current_model, play_config=self.config.eval.play_config) ng_player = ChessPlayer(self.config, model=ng_model, play_config=self.config.eval.play_config) if current_white: white, black = current_player, ng_player else: white, black = ng_player, current_player while not env.done: if env.board.turn == chess.WHITE: action = white.action(env) else: action = black.action(env) env.step(action) if env.num_halfmoves >= self.config.eval.max_game_length: env.adjudicate() if env.winner == Winner.draw: ng_score = 0.5 elif env.whitewon == current_white: ng_score = 0 else: ng_score = 1 return ng_score, env
def play_game(config, cur, ng, current_white: bool) -> (float, ChessEnv, bool): cur_pipes = cur.pop() ng_pipes = ng.pop() env = ChessEnv().reset() current_player = ChessPlayer(config, pipes=cur_pipes, play_config=config.eval.play_config) ng_player = ChessPlayer(config, pipes=ng_pipes, play_config=config.eval.play_config) if current_white: white, black = current_player, ng_player else: white, black = ng_player, current_player 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.eval.max_game_length: env.adjudicate() if env.winner == Winner.draw: ng_score = 0.5 elif env.white_won == current_white: ng_score = 0 else: ng_score = 1 cur.append(cur_pipes) ng.append(ng_pipes) return ng_score, env, current_white
async def start_search_my_move(self, board): self.running_simulation_num += 1 with await self.sem: # reduce parallel search number env = ChessEnv().update(board) leaf_v = await self.search_my_move(env, is_root_node=True) self.running_simulation_num -= 1 return leaf_v
def play_game(self, best_model, ng_model): env = ChessEnv().reset() best_player = ChessPlayer(self.config, best_model, play_config=self.config.eval.play_config) ng_player = ChessPlayer(self.config, ng_model, play_config=self.config.eval.play_config) best_is_white = random() < 0.5 if not best_is_white: black, white = best_player, ng_player else: black, white = ng_player, best_player observation = env.observation while not env.done: if env.board.turn == chess.BLACK: action = black.action(observation) else: action = white.action(observation) board, info = env.step(action) observation = board.fen() ng_win = None if env.winner == Winner.white: if best_is_white: ng_win = 0 else: ng_win = 1 elif env.winner == Winner.black: if best_is_white: ng_win = 1 else: ng_win = 0 return ng_win, best_is_white
def action(self, board): env = ChessEnv().update(board) key = self.counter_key(env) for tl in range(self.play_config.thinking_loop): if tl > 0 and self.play_config.logging_thinking: logger.debug( f"continue thinking: policy move=({action % 8}, {action // 8}), " f"value move=({action_by_value % 8}, {action_by_value // 8})" ) self.search_moves(board) policy = self.calc_policy(board) action = int(np.random.choice(range(self.labels_n), p=policy)) action_by_value = int( np.argmax(self.var_q[key] + (self.var_n[key] > 0) * 100)) if action == action_by_value or env.turn < self.play_config.change_tau_turn: break # this is for play_gui, not necessary when training. self.thinking_history[env.observation] = HistoryItem( action, policy, list(self.var_q[key]), list(self.var_n[key])) if self.play_config.resign_threshold is not None and \ env.score_current() <= self.play_config.resign_threshold and \ self.play_config.min_resign_turn < env.turn: return None # means resign else: self.moves.append([env.observation, list(policy)]) return self.config.labels[action]
def start(config: Config): PlayWithHumanConfig().update_play_config(config.play) chess_model = PlayWithHuman(config) env = ChessEnv().reset() human_is_black = random() < 0.5 chess_model.start_game(human_is_black) while not env.done: if env.board.turn == chess.BLACK: if not human_is_black: action = chess_model.move_by_ai(env) print("IA moves to: " + action) else: action = chess_model.move_by_human(env) print("You move to: " + action) else: if human_is_black: action = chess_model.move_by_ai(env) print("IA moves to: " + action) else: action = chess_model.move_by_human(env) print("You move to: " + action) board, info = env.step(action) env.render() print("Board fen = " + board.fen()) print("\nEnd of the game.") print("Game result:") print(env.board.result())
def start(config: Config): PlayWithHumanConfig().update_play_config(config.play) me_player = None env = ChessEnv().reset() app = Flask(__name__) model = ChessModel(config) if not load_best_model_weight(model): raise RuntimeError("Best model not found!") player = ChessPlayer(config, model.get_pipes(config.play.search_threads)) @app.route('/play', methods=["GET", "POST"]) def play(): data = request.get_json() print(data["position"]) env.update(data["position"]) env.step(data["moves"], False) bestmove = player.action(env, False) return jsonify(bestmove) app.run(host="0.0.0.0", port="8080")
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 training(self): tc = self.config.trainer last_load_data_step = last_save_step = total_steps = self.config.trainer.start_total_steps meta_dir = 'data/model' meta_file = os.path.join(meta_dir, 'metadata.json') file_dir = 'data/model/next_generation' h5_file = os.path.join(file_dir, 'weights.{epoch:02d}.h5') self.meta_writer = OptimizeWorker(meta_file) self.early_stopping = EarlyStopping(monitor='val_loss') self.check_point = ModelCheckpoint(filepath=h5_file, monitor='val_loss', verbose=1) while True: self.load_play_data() if (self.dataset_size * (1 - self.validation)) < tc.batch_size: while (self.dataset_size * (1 - self.validation)) < tc.batch_size: self_play = SelfPlayWorker(self.config, env=ChessEnv(), model=self.model) self_play.start() self.load_play_data() else: self_play = SelfPlayWorker(self.config, env=ChessEnv(), model=self.model) self_play.start() self.load_play_data() self.compile_model() self.update_learning_rate(total_steps) steps = self.train_epoch(self.config.trainer.epoch_to_checkpoint) total_steps += steps if True: self.save_current_model() last_save_step = total_steps #net_params = ChessModel(self.config).get_policy_param() #pickle.dump(net_params, open('current_policy.model', 'wb'), pickle.HIGHEST_PROTOCOL) k.clear_session() load_best_model_weight(self.model)
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 convert_to_training_data(data): """ :param data: format is SelfPlayWorker.buffer :return: """ state_list = [] policy_list = [] z_list = [] aux_move_number = 1 movements = [] for state, policy, z in data: move_number = int( (ChessEnv().update(state, movements)).board.fen().split(" ")[5]) if aux_move_number < move_number: if len(movements) > 8: movements.pop(0) movements.append(env.observation) aux_move_number = move_number else: aux_move_number = 1 movements = [] env = ChessEnv().update(state, movements) black_ary, white_ary, current_player, move_number = env.black_and_white_plane( ) state = [black_ary, white_ary ] if env.board.fen().split(" ")[1] == 'b' else [ white_ary, black_ary ] state = np.reshape(np.reshape(np.array(state), (18, 6, 8, 8)), (108, 8, 8)) state = np.vstack((state, np.reshape(current_player, (1, 8, 8)), np.reshape(move_number, (1, 8, 8)))) state_list.append(state) policy_list.append(policy) z_list.append(z) return np.array(state_list), np.array(policy_list), np.array(z_list)
def convert_to_cheating_data(data): """ :param data: format is SelfPlayWorker.buffer :return: """ state_list = [] policy_list = [] value_list = [] env = ChessEnv().reset() for state_fen, policy, value in data: move_number = int(state_fen.split(' ')[5]) # f2 = maybe_flip_fen(maybe_flip_fen(state_fen,True),True) # assert state_fen == f2 next_move = env.deltamove(state_fen) if next_move == None: # new game! assert state_fen == chess.STARTING_FEN env.reset() else: env.step(next_move, False) state_planes = env.canonical_input_planes() # assert env.check_current_planes(state_planes) side_to_move = state_fen.split(" ")[1] if side_to_move == 'b': #assert np.sum(policy) == 0 policy = Config.flip_policy(policy) else: #assert abs(np.sum(policy) - 1) < 1e-8 pass # if np.sum(policy) != 0: # policy /= np.sum(policy) #assert abs(np.sum(policy) - 1) < 1e-8 assert len(policy) == 1968 assert state_planes.dtype == np.float32 value_certainty = min( 15, move_number ) / 15 # reduces the noise of the opening... plz train faster SL_value = value * value_certainty + env.testeval() * (1 - value_certainty) state_list.append(state_planes) policy_list.append(policy) value_list.append(SL_value) return np.array(state_list, dtype=np.float32), np.array( policy_list, dtype=np.float32), np.array(value_list, dtype=np.float32)
def sl_action(self, board, action): env = ChessEnv().update(board) policy = np.zeros(self.labels_n) k = 0 for mov in self.config.labels: if mov == action: policy[k] = 1.0 break k += 1 self.moves.append([env.observation, list(policy)]) return action
def calc_policy(self, board): """calc π(a|s0) :return: """ pc = self.play_config env = ChessEnv().update(board) key = self.counter_key(env) if env.turn < pc.change_tau_turn: return self.var_n[key] / (np.sum(self.var_n[key])+1e-8) # tau = 1 else: action = np.argmax(self.var_n[key]) # tau = 0 ret = np.zeros(self.labels_n) ret[action] = 1 return ret
def start(config: Config): PlayWithHumanConfig().update_play_config(config.play) me_player = None env = ChessEnv().reset() while True: line = input() words = line.rstrip().split(" ", 1) if words[0] == "uci": print("id name ChessZero") print("id author ChessZero") print("uciok") elif words[0] == "isready": if not me_player: me_player = get_player(config) print("readyok") elif words[0] == "ucinewgame": env.reset() elif words[0] == "position": words = words[1].split(" ", 1) if words[0] == "startpos": env.reset() else: if words[0] == "fen": # skip extraneous word words = words[1].split(' ', 1) fen = words[0] for _ in range(5): words = words[1].split(' ', 1) fen += " " + words[0] env.update(fen) if len(words) > 1: words = words[1].split(" ", 1) if words[0] == "moves": for w in words[1].split(" "): env.step(w, False) elif words[0] == "go": if not me_player: me_player = get_player(config) action = me_player.action(env, False) print(f"bestmove {action}") elif words[0] == "stop": pass elif words[0] == "quit": break
def play_game(config, cur, ng, current_white: bool) -> (float, ChessEnv, bool): """ Plays a game against models cur and ng and reports the results. :param Config config: config for how to play the game :param ChessModel cur: should be the current model :param ChessModel ng: should be the next generation model :param bool current_white: whether cur should play white or black :return (float, ChessEnv, bool): the score for the ng model (0 for loss, .5 for draw, 1 for win), the env after the game is finished, and a bool which is true iff cur played as white in that game. """ cur_pipes = cur.pop() ng_pipes = ng.pop() env = ChessEnv().reset() current_player = ChessPlayer(config, pipes=cur_pipes, play_config=config.eval.play_config) ng_player = ChessPlayer(config, pipes=ng_pipes, play_config=config.eval.play_config) if current_white: white, black = current_player, ng_player else: white, black = ng_player, current_player 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.eval.max_game_length: env.adjudicate() if env.winner == Winner.draw: ng_score = 0.5 elif env.white_won == current_white: ng_score = 0 else: ng_score = 1 cur.append(cur_pipes) ng.append(ng_pipes) return ng_score, env, current_white
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 start(config: Config): PlayWithHumanConfig().update_play_config(config.play) config.play.thinking_loop = 1 chess_model = None env = ChessEnv().reset() while True: line = input() words = line.rstrip().split(" ", 1) if words[0] == "uci": print("id name ChessZero") print("id author ChessZero") print("uciok") elif words[0] == "isready": if chess_model is None: chess_model = PlayWithHuman(config) print("readyok") elif words[0] == "ucinewgame": env.reset() elif words[0] == "position": words = words[1].split(" ", 1) if words[0] == "startpos": env.reset() else: fen = words[0] for _ in range(5): words = words[1].split(' ', 1) fen += " "+words[0] env.update(fen) if len(words) > 1: words = words[1].split(" ", 1) if words[0] == "moves": for w in words[1].split(" "): env.step(w, False) elif words[0] == "go": action = chess_model.move_by_ai(env) print(f"bestmove {action}") elif words[0] == "stop": pass elif words[0] == "quit": break
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
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() 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 convert_to_training_data(data): """ :param data: format is SelfPlayWorker.buffer :return: """ state_list = [] policy_list = [] z_list = [] for state, policy, z in data: env = ChessEnv().update(state) black_ary, white_ary = env.black_and_white_plane() state = [black_ary, white_ary] if env.board.turn == chess.BLACK else [white_ary, black_ary] state_list.append(state) policy_list.append(policy) z_list.append(z) return np.array(state_list), np.array(policy_list), np.array(z_list)
def start(config: Config): PlayWithHumanConfig().update_play_config(config.play) chess_model = PlayWithEngine(config) env = ChessEnv().reset() human_is_black = random() < 0.5 chess_model.start_game(human_is_black) while not env.done: if (env.board.turn == chess.BLACK) == human_is_black: action = chess_model.move_by_opponent(env) print("You move to: " + action) else: action = chess_model.move_by_ai(env) print("AI moves to: " + action) board, info = env.step(action) env.render() print("Board FEN = " + board.fen()) print("\nEnd of the game.") #spaces after this? print("Game result:") #and this? print(env.board.result())
def play(self, request, context): try: # In our case, request is a Input() object (from .proto file) self.uid = request.uid # Get a random generated UID (hash) if self.uid not in CHESS_ENV_DICT: if self.uid == "": self.uid = generate_uid() chess_env = ChessEnv().reset() log.debug("UID created: {}".format(self.uid)) else: chess_env = CHESS_ENV_DICT[self.uid] log.debug("UID exists: {}".format(self.uid)) # Check if a command was sent. self.cmd = request.cmd if self.cmd == "finish": log.debug("CMD [finish]: {}".format(self.uid)) if self.uid in CHESS_ENV_DICT: del CHESS_ENV_DICT[self.uid] self.response.status = "game_over by finish command".encode("utf-8") else: self.response.status = "no game for this UID: {}".format(self.uid).encode("utf-8") return self.response elif self.cmd == "restart": chess_env = ChessEnv().reset() log.debug("CMD [restart]: {}".format(self.uid)) self.move = request.move manager = multiprocessing.Manager() return_dict = manager.dict() p = multiprocessing.Process(target=mp_play, args=(self.move, self.cmd, chess_env, return_dict)) p.start() p.join() chess_env, response = return_dict.get("response", (None, None)) if not response or "error" in response: error_msg = response.get("error", None) if response else None log.error(error_msg) context.set_details(error_msg) context.set_code(grpc.StatusCode.INTERNAL) return Output() # Game over if chess_env is None: del CHESS_ENV_DICT[self.uid] else: # Update the board state for current UID CHESS_ENV_DICT[self.uid] = chess_env board = "" for idx, line in enumerate(response["board"]): board += "{}\n".format(line) log.debug("play({},{}):\n{}\n{}\n{}".format( self.move, self.cmd, self.uid, board, response["status"])) return Output(uid=self.uid, board=board, status=response["status"]) except Exception as e: traceback.print_exc() log.error(e) return Output(status="Fail")
def start(config: Config): tf_util.set_session_config(per_process_gpu_memory_fraction=0.5) return SelfPlayWorker(config, env=ChessEnv()).start()
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
def start(config: Config): return SupervisedLearningWorker(config, env=ChessEnv(config)).start()
def start(config: Config): tf_util.set_session_config(per_process_gpu_memory_fraction=0.1) return SupervisedLearningWorker(config, env=ChessEnv()).start()