import openai import chess import chess.engine import os import csv import random import time import platform # NOTE: LLAMA AND NANOGPT ARE EXPERIMENTAL PLAYERS, if not using them, comment them out # from llama_module import BaseLlamaPlayer, LocalLlamaPlayer, LocalLoraLlamaPlayer from nanogpt.nanogpt_module import NanoGptPlayer from mamba_module import MambaPlayer import gpt_query from lczero.backends import Weights, Backend, GameState import numpy as np from typing import Optional, Tuple from dataclasses import dataclass @dataclass class LegalMoveResponse: move_san: Optional[str] = None move_uci: Optional[chess.Move] = None attempts: int = 0 is_resignation: bool = False is_illegal_move: bool = False # Define base Player class class Player: def get_move(self, board: chess.Board, game_state: str, temperature: float) -> str: raise NotImplementedError def get_config(self) -> dict: raise NotImplementedError class GPTPlayer(Player): def __init__(self, model: str): with open("gpt_inputs/api_key.txt", "r") as f: openai.api_key = f.read().strip() self.model = model def get_move( self, board: chess.Board, game_state: str, temperature: float ) -> Optional[str]: response = get_gpt_response(game_state, self.model, temperature) return get_move_from_gpt_response(response) def get_config(self) -> dict: return {"model": self.model} class LC0PLayer(Player): # "11258-32x4-se.pb.gz" = stockfish level 0- = skill 0 # "11258-48x5-se.pb.gz" = stockfish level 0+ = skill 1 # "11258-80x7-se.pb.gz" = stockfish level 1 = skill 2 # "11258-104x9-se.pb.gz" = stockfish level 2 = skill 3 # "TK-6430 aka 128x10-BPR-64M-6430000.pb.gz" = stockfish level 3 = skill 4 # "00af53b081e80147172e6f281c01daf5ca19ada173321438914c730370aa4267" = stockfish level 4 = skill 5 # "b2ec465d0fb5b5eb39d2e1e3f74041a5d2fc92d413b71aa7ea0b6fb082ccba9c" = stockfish level 5+ = skill 6 def __init__(self, skill): self.skill = skill network_paths = ["./lc0/build/release/11258-32x4-se.pb.gz", "./lc0/build/release/11258-48x5-se.pb.gz", "./lc0/build/release/11258-80x7-se.pb.gz", "./lc0/build/release/11258-104x9-se.pb.gz", "./lc0/build/release/TK-6430 aka 128x10-BPR-64M-6430000.pb.gz", "./lc0/build/release/00af53b081e80147172e6f281c01daf5ca19ada173321438914c730370aa4267", "./lc0/build/release/b2ec465d0fb5b5eb39d2e1e3f74041a5d2fc92d413b71aa7ea0b6fb082ccba9c"] print(f"\n\nLoading lc0 network: {network_paths[skill]}\n\n") self.weights = Weights(network_paths[skill]) self.backend = Backend(weights=self.weights) self.gamestate = GameState() def get_move(self, board: chess.Board, game_state: str, temperature: float): self.gamestate = GameState(fen=board.fen()) input_planes = self.gamestate.as_input(self.backend) result = self.backend.evaluate(input_planes)[0] moves = self.gamestate.moves() policy_indices = self.gamestate.policy_indices() move_probs = np.array(result.p_softmax(*policy_indices)) best_move_idx = move_probs.argmax() best_move = moves[best_move_idx] return board.san(chess.Move.from_uci(best_move)) def get_config(self) -> dict: return {"network": self.weights, "skill_level": self.skill, "play_time": 0} class StockfishPlayer(Player): @staticmethod def get_stockfish_path() -> str: """ Determines the operating system and returns the appropriate path for Stockfish. Returns: str: Path to the Stockfish executable based on the operating system. """ if platform.system() == 'Linux': return "/usr/games/stockfish" elif platform.system() == 'Darwin': # Darwin is the system name for macOS return "stockfish" elif platform.system() == 'Windows': return r"C:\Users\Haile\Downloads\stockfish\stockfish-windows-x86-64-avx2.exe" else: raise OSError("Unsupported operating system") def __init__(self, skill_level: int, play_time: float): self._skill_level = skill_level self._play_time = play_time # If getting started, you need to run brew install stockfish stockfish_path = StockfishPlayer.get_stockfish_path() self._engine = chess.engine.SimpleEngine.popen_uci(stockfish_path) def get_move( self, board: chess.Board, game_state: str, temperature: float ) -> Optional[str]: if self._skill_level == -2: legal_moves = list(board.legal_moves) random_move = random.choice(legal_moves) return board.san(random_move) elif self._skill_level < 0: self._engine.configure({"Skill Level": 0}) result = self._engine.play( board, chess.engine.Limit(time=1e-8, depth=1, nodes=1) ) else: self._engine.configure({"Skill Level": self._skill_level}) result = self._engine.play(board, chess.engine.Limit(time=self._play_time)) if result.move is None: return None return board.san(result.move) def get_config(self) -> dict: return {"skill_level": self._skill_level, "play_time": self._play_time} def close(self): self._engine.quit() class HumanPlayer(Player): def get_move(self, board: chess.Board, game_state: str, temperature: float) -> str: # Print board for human player print(board) while True: move = input("Enter your move (SAN format): ") try: move_uci = board.parse_san(move) if move_uci in board.legal_moves: return move except: print("Illegal move, try again.") def get_config(self) -> dict: return {"player": "human"} def get_gpt_response(game_state: str, model: str, temperature: float) -> Optional[str]: # trying to prevent what I believe to be rate limit issues if model == "gpt-4": time.sleep(0.4) response = gpt_query.get_gpt_response(game_state, model, temperature) return response def get_move_from_gpt_response(response: Optional[str]) -> Optional[str]: if response is None: return None # Parse the response to get only the first move moves = response.split() first_move = moves[0] if moves else None return first_move def calculate_stats(csv_file_path): data = [] with open(csv_file_path, "r") as csv_file: reader = csv.DictReader(csv_file) data = list(reader) if not data: return None stats = { "wins": sum(float(row["player_one_score"]) for row in data if float(row["player_one_score"]) > 0.6), "draws": len(data) - sum(float(row["player_two_score"]) for row in data if float(row["player_two_score"]) > 0.6) - sum(float(row["player_one_score"]) for row in data if float(row["player_one_score"]) > 0.6), "illegal_attempts_ratio": sum(float(row["p1_illegal_attempts"]) for row in data) / (sum(float(row["p1_illegal_attempts"]) for row in data) + sum(float(row["player_one_legal_moves"]) for row in data)), "illegal_moves_ratio": sum(float(row["player_one_illegal_moves"]) for row in data) / sum(float(row["player_one_illegal_moves"]) + float(row["player_one_legal_moves"]) for row in data), "avg_attempts_per_illegal": sum(float(row["p1_avg_attempts_per_illegal"]) for row in data) / len(data), "avg_first_illegal_move": sum(float(row["p1_first_illegal_move_num"]) for row in data if float(row["p1_first_illegal_move_num"]) > 0) / len([row for row in data if float(row["p1_first_illegal_move_num"]) > 0]), "avg_illegal_move_num": sum(float(row["p1_avg_illegal_move_num"]) for row in data if float(row["p1_avg_illegal_move_num"]) > 0) / len([row for row in data if float(row["p1_avg_illegal_move_num"]) > 0]), "lost_to_illegal_ratio": len([row for row in data if row["player_one_failed_to_find_legal_move"] == "True"]) / len([row for row in data if float(row["number_of_moves"]) > 0]), "avg_game_length": sum(float(row["number_of_moves"]) for row in data) / len(data), "max_game_length": max(float(row["number_of_moves"]) for row in data), } return stats def record_results( board: chess.Board, player_one: Player, player_two: Player, game_state: str, player_one_illegal_moves: int, player_one_illegal_attempts: int, player_two_illegal_moves: int, player_one_legal_moves: int, player_two_legal_moves: int, total_time: float, player_one_resignation: bool, player_two_resignation: bool, player_one_failed_to_find_legal_move: bool, player_two_failed_to_find_legal_move: bool, total_moves: int, illegal_moves: int, opening_moves: int, illegal_move_numbers: list[int] ): unique_game_id = generate_unique_game_id() ( player_one_title, player_two_title, player_one_time, player_two_time, ) = get_player_titles_and_time(player_one, player_two) if player_one_resignation or player_one_failed_to_find_legal_move: result = "0-1" player_one_score = 0 player_two_score = 1 elif player_two_resignation or player_two_failed_to_find_legal_move: result = "1-0" player_one_score = 1 player_two_score = 0 else: result = board.result() # Hmmm.... debating this one. Annoying if I leave it running and it fails here for some reason, probably involving some # resignation / failed move situation I didn't think of # -1e10 at least ensures it doesn't fail silently if "-" in result: player_one_score = result.split("-")[0] player_one_score = 0.5 if player_one_score == "1/2" else player_one_score player_two_score = result.split("-")[1] player_two_score = 0.5 if player_two_score == "1/2" else player_two_score elif result == "*": # Loss due to hitting max moves player_one_score = 0 player_two_score = 1 else: player_one_score = -1e10 player_two_score = -1e10 played_moves = player_one_illegal_moves + player_one_legal_moves info_dict = { "game_id": unique_game_id, "transcript": game_state, "result": result, "player_one": player_one_title, "player_two": player_two_title, "player_one_time": player_one_time, "player_two_time": player_two_time, "player_one_score": player_one_score, "player_two_score": player_two_score, "player_one_illegal_moves": player_one_illegal_moves, "player_two_illegal_moves": player_two_illegal_moves, "player_one_legal_moves": player_one_legal_moves, "player_two_legal_moves": player_two_legal_moves, "player_one_resignation": player_one_resignation, "player_two_resignation": player_two_resignation, "player_one_failed_to_find_legal_move": player_one_failed_to_find_legal_move, "player_two_failed_to_find_legal_move": player_two_failed_to_find_legal_move, "game_title": f"{player_one_title} vs. {player_two_title}", "number_of_moves": board.fullmove_number, "p1_illegal_attempts": player_one_illegal_attempts, "p1_avg_attempts_per_illegal": 0 if player_one_illegal_moves == 0 else player_one_illegal_attempts / float(player_one_illegal_moves), "p1_illegal_attemtps_pct": 1.0 if played_moves == 0 else player_one_illegal_attempts / float(player_one_illegal_attempts + player_one_legal_moves), "p1_illegal_moves_pct": 1.0 if played_moves == 0 else player_one_illegal_moves / float(played_moves), "p1_first_illegal_move_num": illegal_move_numbers[0] if illegal_move_numbers else 0, "p1_avg_illegal_move_num": np.average(illegal_move_numbers) if illegal_move_numbers else 0, "time_taken": total_time, "total_moves": total_moves, "illegal_moves": illegal_moves, } if RUN_FOR_ANALYSIS: csv_file_path = f"logs/{player_one_recording_name}_vs_{player_two_recording_name}" csv_file_path = csv_file_path.replace(".", "_") # Because I'm using ckpt filenames for nanogpt models csv_file_path += ".csv" else: csv_file_path = recording_file # Determine if we need to write headers (in case the file doesn't exist yet) write_headers = not os.path.exists(csv_file_path) # Append the results to the CSV file os.makedirs(os.path.dirname(csv_file_path), exist_ok=True) with open(csv_file_path, "a", newline="") as csv_file: writer = csv.DictWriter(csv_file, fieldnames=info_dict.keys()) if write_headers: writer.writeheader() writer.writerow(info_dict) with open("game.txt", "w") as f: f.write(game_state) def generate_unique_game_id() -> str: timestamp = int(time.time()) random_num = random.randint(1000, 9999) # 4-digit random number return f"{timestamp}-{random_num}" def get_player_titles_and_time( player_one: Player, player_two: Player ) -> Tuple[str, str, Optional[float], Optional[float]]: player_one_config = player_one.get_config() player_two_config = player_two.get_config() # For player one if "model" in player_one_config: player_one_title = player_one_config["model"] player_one_time = None else: player_one_title = f"Stockfish {player_one_config['skill_level']}" player_one_time = player_one_config["play_time"] # For player two if "model" in player_two_config: player_two_title = player_two_config["model"] player_two_time = None else: player_two_title = f"Stockfish {player_two_config['skill_level']}" player_two_time = player_two_config["play_time"] return (player_one_title, player_two_title, player_one_time, player_two_time) used_openings = [] def random_book_opening( game_state: str, board: chess.Board ) -> Tuple[str, chess.Board]: global used_openings with open("openings.csv", "r") as file: lines = file.readlines()[1:] # Skip header moves_string = random.choice(lines) while moves_string in used_openings: moves_string = random.choice(lines) used_openings.append(moves_string) if move_num_in_gamestate: game_state = moves_string.rstrip() + " " else: game_state = ' '.join(['.' + m.split(".")[-1] if "." in m else m for m in moves_string.split()]) game_state = game_state.rstrip() + " " # Splitting the moves string on spaces tokens = moves_string.split() for token in tokens: # If the token contains a period, it's a move number + move combination if "." in token: move = token.split(".")[-1] # Take the move part after the period else: move = token board.push_san(move) return game_state.rstrip(), board, len(tokens) // 2 def add_random_moves( game_state: str, board: chess.Board, num_moves: int = 20 ) -> Tuple[str, chess.Board, int]: for i in range(num_moves * 2): # Full moves to half moves legal_moves = list(board.legal_moves) if not legal_moves: print("Random moves: no legal moves left.") return None, None, 0 # Game over, discard the game move = board.san(random.choice(legal_moves)) board.push(board.parse_san(move)) if board.turn == chess.BLACK: game_state += f" {i//2 + 1}.{move}" if move_num_in_gamestate else f" .{move}" else: game_state += f" {move}" if board.is_game_over(): print("Random moves: game over.") return None, None, 0 # Game over, discard the game game_state = game_state.strip() print(f"{num_moves} Random moves added, returning: {game_state}") return game_state, board, num_moves def evaluate_position(fen, backend): gamestate = GameState(fen=fen) result = backend.evaluate(gamestate.as_input(backend))[0] return result.q() def material_balance(board): PV = { 'pawn': 1, 'knight': 3, 'bishop': 3, 'rook': 5, 'queen': 9, 'king': 0 } if board.is_insufficient_material(): return 0 wp = len(board.pieces(chess.PAWN, chess.WHITE)) bp = len(board.pieces(chess.PAWN, chess.BLACK)) wn = len(board.pieces(chess.KNIGHT, chess.WHITE)) bn = len(board.pieces(chess.KNIGHT, chess.BLACK)) wb = len(board.pieces(chess.BISHOP, chess.WHITE)) bb = len(board.pieces(chess.BISHOP, chess.BLACK)) wr = len(board.pieces(chess.ROOK, chess.WHITE)) br = len(board.pieces(chess.ROOK, chess.BLACK)) wq = len(board.pieces(chess.QUEEN, chess.WHITE)) bq = len(board.pieces(chess.QUEEN, chess.BLACK)) return ( PV['pawn'] * (wp - bp) + PV['knight'] * (wn - bn) + PV['bishop'] * (wb - bb) + PV['rook'] * (wr - br) + PV['queen'] * (wq - bq) ) # Return is (move_san, move_uci, attempts, is_resignation, is_illegal_move) def get_legal_move( player: Player, board: chess.Board, game_state: str, player_one: bool, max_attempts: int = 5, ) -> LegalMoveResponse: """Request a move from the player and ensure it's legal.""" move_san = None move_uci = None for attempt in range(max_attempts): #print(f"get_legal_move: |{game_state}|") move_san = player.get_move( board, game_state, min(((attempt / max_attempts) * 1) + 0.001, 0.75) ) # Sometimes when GPT thinks it's the end of the game, it will just output the result # Like "1-0". If so, this really isn't an illegal move, so we'll add a check for that. if move_san is not None: if move_san == "1-0" or move_san == "0-1" or move_san == "1/2-1/2": print(f"{move_san}, player has resigned") return LegalMoveResponse( move_san=None, move_uci=None, attempts=attempt, is_resignation=True, ) try: move_uci = board.parse_san(move_san) except Exception as e: print(f"Error parsing move {move_san}: {e}") # check if player is gpt-3.5-turbo-instruct # only recording errors for gpt-3.5-turbo-instruct because it's errors are so rare if player.get_config()["model"] == "gpt-3.5-turbo-instruct": with open("gpt-3.5-turbo-instruct-illegal-moves.txt", "a") as f: f.write(f"{game_state}\n{move_san}\n") continue if move_uci in board.legal_moves: if player_one == False: if not move_san.startswith(" "): move_san = " " + move_san else: if move_san.startswith(" "): move_san = move_san[1:] return LegalMoveResponse(move_san, move_uci, attempt) print(f"Illegal move: {move_san}") # If we reach here, the player has made illegal moves for all attempts. print(f"{player} provided illegal moves for {max_attempts} attempts.") return LegalMoveResponse( move_san=None, move_uci=None, attempts=max_attempts, is_illegal_move=True ) def play_turn( player: Player, board: chess.Board, game_state: str, player_one: bool ) -> Tuple[str, bool, bool, int]: result = get_legal_move(player, board, game_state, player_one, 5) illegal_moves = result.attempts move_san = result.move_san move_uci = result.move_uci resignation = result.is_resignation failed_to_find_legal_move = result.is_illegal_move if resignation: print(f"{player} resigned with result: {board.result()}") elif failed_to_find_legal_move: print(f"Game over: 5 consecutive illegal moves from {player}") elif move_san is None or move_uci is None: print(f"Game over: {player} failed to find a legal move") else: board.push(move_uci) game_state += move_san print(move_san, end=" ") return game_state, resignation, failed_to_find_legal_move, illegal_moves def play_games( player_one: Player, player_two: Player, max_games: int = 10, book_opening: bool = False, random_opening: bool = False, random_opening_moves: int = 20, random_move_start: int = 0, ): unique_games = set() games_saved = 0 while games_saved < max_games: print(f"\nGame {games_saved} of {max_games}\n") # with open("gpt_inputs/prompt.txt", "r") as f: # game_state = f.read() game_state = "" board = chess.Board() if book_opening: game_state, board, opening_moves = random_book_opening(game_state, board) elif random_opening: for _ in range(10): g, b, opening_moves = add_random_moves(game_state, board, random_opening_moves) if g is not None: game_state = g board = b break else: opening_moves = 0 player_one_illegal_moves = 0 player_one_illegal_attempts = 0 player_two_illegal_moves = 0 player_one_legal_moves = 0 player_two_legal_moves = 0 player_one_resignation = False player_two_resignation = False player_one_failed_to_find_legal_move = False player_two_failed_to_find_legal_move = False start_time = time.time() total_moves = 0 illegal_moves = 0 illegal_move_numbers = [] print_for_human = isinstance(player_one, HumanPlayer) or isinstance(player_two, HumanPlayer) while not board.is_game_over(): if print_for_human: print(board) with open("game.txt", "w") as f: f.write(game_state) current_move_num = f"{board.fullmove_number if move_num_in_gamestate else ''}." #if total_moves == random_move_start: # for _ in range(10): # g, b, opening_moves = add_random_moves(game_state, board, random_opening_moves) # if g is not None: # game_state = g # board = b # break # total_moves += random_opening_moves # continue total_moves += 1 # I increment legal moves here so player_two isn't penalized for the game ending before its turn player_one_legal_moves += 1 player_two_legal_moves += 1 # this if statement may be overkill, just trying to get format to exactly match PGN notation if board.fullmove_number != 1: game_state += " " game_state += current_move_num #print(f"|{game_state}|") #print(f"{current_move_num}", end=" ") if update_linear or eval_linear: prev_q_value = evaluate_position(board.fen(), player_two.backend) ( game_state, player_one_resignation, player_one_failed_to_find_legal_move, illegal_moves_one, ) = play_turn(player_one, board, game_state, player_one=True) player_one_illegal_moves += 1 if illegal_moves_one > 0 else 0 player_one_illegal_attempts += illegal_moves_one if illegal_moves_one != 0: player_one_legal_moves -= 1 illegal_move_numbers.append(board.fullmove_number) if update_activations or update_linear or eval_linear: player_one.update_activations("current") if ( board.is_game_over() or player_one_resignation or player_one_failed_to_find_legal_move ): break if update_linear or eval_linear: curr_q_value = evaluate_position(board.fen(), player_two.backend) q_value_delta = curr_q_value - prev_q_value material_bal = material_balance(board) player_one.update_linear_probe_targets(curr_q_value, q_value_delta, material_bal) if update_linear: player_one.train_linear_probes() if eval_linear: player_one.evaluate_linear_probes(board) player_one.update_activations("reset") ( game_state, player_two_resignation, player_two_failed_to_find_legal_move, illegal_moves_two, ) = play_turn(player_two, board, game_state, player_one=False) player_two_illegal_moves += 1 if illegal_moves_two > 0 else 0 if illegal_moves_two != 0: player_two_legal_moves -= 1 if ( board.is_game_over() or player_two_resignation or player_two_failed_to_find_legal_move ): break print("\n", end="") if total_moves > MAX_MOVES: break end_time = time.time() total_time = end_time - start_time print(f"\nGame over. Total time: {total_time} seconds") print(f"Result: {board.result()}") print(board) print() game_transcript = game_state.strip() if game_transcript not in unique_games: unique_games.add(game_transcript) record_results( board, player_one, player_two, game_state, player_one_illegal_moves, player_one_illegal_attempts, player_two_illegal_moves, player_one_legal_moves, player_two_legal_moves, total_time, player_one_resignation, player_two_resignation, player_one_failed_to_find_legal_move, player_two_failed_to_find_legal_move, total_moves, illegal_moves, opening_moves, illegal_move_numbers ) games_saved += 1 #if update_linear: # player_one.train_linear_probes() if update_activations: if player_one_resignation or player_one_failed_to_find_legal_move: player_one.update_activations("lost") elif player_two_resignation or player_two_failed_to_find_legal_move: player_one.update_activations("won") else: if board.result() == "1-0": player_one.update_activations("won") elif board.result() == "0-1": player_one.update_activations("lost") if games_saved % save_activations_every == 0: player_one.save_activations(activations_path) elif update_linear: player_one.update_activations("reset") if update_linear and games_saved % save_activations_every == 0: player_one.save_linear_probe_data(linear_path) else: print("Duplicate game; not saved.") if isinstance(player_one, StockfishPlayer): player_one.close() if isinstance(player_two, StockfishPlayer): player_two.close() if RUN_FOR_ANALYSIS: csv_file_path = f"logs/{player_one_recording_name}_vs_{player_two_recording_name}" csv_file_path = csv_file_path.replace(".", "_") # Because I'm using ckpt filenames for nanogpt models csv_file_path += ".csv" else: csv_file_path = recording_file stats = calculate_stats(csv_file_path) if stats: print("\nStatistics:") for key, value in stats.items(): print(f"{key}: {value}") with open(csv_file_path, "a") as csv_file: writer = csv.writer(csv_file) writer.writerow([""] * 19) # Add empty cells for existing columns writer.writerow(list(stats.keys())) writer.writerow(list(stats.values())) RUN_FOR_ANALYSIS = True MAX_MOVES = 60 #999 # Due to nanogpt max input length of 1024 recording_file = "logs/determine.csv" # default recording file. Because we are using list [player_ones], recording_file is overwritten player_ones = ["50M/anneal/anneal_complete_round3.pt"] player_two_recording_name = "lc0_sweep" #"stockfish_sweep" move_num_in_gamestate = False book_opening = True random_opening = False random_opening_moves = 10 activations_path="activations_rdm.pkl" update_activations = False apply_activations = False save_activations_every = 25 contrastive_weight = 0.8 linear_path="linear.pkl" update_linear = False eval_linear = True if __name__ == "__main__": for nanogpt_player in player_ones: i = 0 rm = 10 # for rm in range(5, 36, 5): for i in [0]: # [3] #range(11): # for wgt in [0.005, 0.01, 0.025, 0.05]: num_games = 5000 # player_one = GPTPlayer(model="gpt-3.5-turbo-instruct") # player_one = LocalLlamaPlayer(model_name="meta-llama/Llama-2-7b-hf") # player_one = LocalLoraLlamaPlayer("meta-llama/Llama-2-7b-hf", "/workspace/axolotl/lora2-out") # player_one = GPTPlayer(model="gpt-4") # player_one = StockfishPlayer(skill_level=-1, play_time=0.1) player_one_recording_name = nanogpt_player # player_one = NanoGptPlayer(model_name=player_one_recording_name, move_num_in_gamestate=move_num_in_gamestate) #player_one_recording_name = f"xformer_rdm_{rm}" player_one = MambaPlayer(model_name=player_one_recording_name, move_num_in_gamestate=move_num_in_gamestate, update_contrastive=update_activations, update_linear=update_linear or eval_linear, linear_probe_path=linear_path) player_one_recording_name = f'linear_train' if apply_activations: player_one.apply_contrastive_activations(path=activations_path, weight=wgt) #player_two = StockfishPlayer(skill_level=i, play_time=0.1) player_two = LC0PLayer(skill=i) # player_two = GPTPlayer(model="gpt-4") # player_two = GPTPlayer(model="gpt-3.5-turbo-instruct") #print(f"\n\nSTARTING GAMES AGAINST STOCKFISH LEVEL {i}\n\n") print(f"\n\nSTARTING GAMES AGAINST LC0 LEVEL {i}\n\n") play_games(player_one, player_two, num_games, book_opening=book_opening, random_opening=random_opening, random_opening_moves=rm)