class PlayWithHuman:
def __init__(self, config: Config):
self.config = config
self.human_color = None
self.observers = []
self.model = self._load_model()
self.ai = None # type: ChessPlayer
self.last_evaluation = None
self.last_history = None # type: HistoryItem
def start_game(self, human_is_black):
self.human_color = chess.BLACK if human_is_black else chess.WHITE
self.ai = ChessPlayer(self.config, self.model)
def _load_model(self):
from chess_zero.agent.model_chess import ChessModel
model = ChessModel(self.config)
if not load_best_model_weight(model):
raise RuntimeError("Best model not found!")
return model
def move_by_ai(self, env):
if self.ai is None:
self.ai = ChessPlayer(self.config, self.model)
action = self.ai.action(env.observation)
self.last_history = self.ai.ask_thought_about(env.observation)
self.last_evaluation = self.last_history.values[
self.last_history.action]
logger.debug(f"Evaluation history by AI = {self.last_history}")
logger.debug(f"Evaluation by AI = {self.last_evaluation}")
return action
def move_by_human(self, env):
while True:
try:
move = input(
'\nEnter your move in UCI format (a1a2, b2b6, ...): ')
if chess.Move.from_uci(move) in env.board.legal_moves:
return move
else:
print("That is NOT a valid move :(.")
except:
print("That is NOT a valid move :(.")
class PlayWithEngine:
def __init__(self, config: Config):
self.config = config
self.engine_color = None
self.engine = UciEngine()
self.observers = []
self.model = self._load_model()
self.ai = None # type: ChessPlayer
self.last_evaluation = None
self.last_history = None # type: HistoryItem
def start_game(self, engine_is_black):
self.engine_color = chess.BLACK if engine_is_black else chess.WHITE
self.engine.start_game(engine_is_black)
self.ai = ChessPlayer(self.config, self.model)
def _load_model(self):
from chess_zero.agent.model_chess import ChessModel
model = ChessModel(self.config)
if not load_best_model_weight(model):
raise RuntimeError("Best model not found!")
return model
def move_by_ai(self, env):
if self.ai is None:
self.ai = ChessPlayer(self.config, self.model)
action = self.ai.action(env.observation)
self.last_history = self.ai.ask_thought_about(env.observation)
self.last_evaluation = self.last_history.values[self.last_history.action]
logger.debug(f"Evaluation history by AI = {self.last_history}")
logger.debug(f"Evaluation by AI = {self.last_evaluation}")
return action
def move_by_opponent(self, env):
self.engine.update_position(env.board)
action = self.engine.best_move()
last_evaluation = self.engine.score()
logger.debug(f"Evaluation by Stockfish = {last_evaluation}")
return action