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"]
# Rare cases where elo ratings are not in the headers
if "WhiteElo" not in game.headers or "BlackElo" not in game.headers:
return None, None, None, None, False
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)
fen_data = []
moves_array = np.zeros(
(len(white.moves) + len(black.moves), white.labels_n),
dtype=np.float16)
scores = np.zeros((len(white.moves) + len(black.moves)), dtype=np.int8)
for i in range(len(white.moves)):
fen_data.append(white.moves[i][0])
moves_array[i * 2] = white.moves[i][1]
scores[i * 2] = white.moves[i][2]
if i < len(black.moves):
fen_data.append(black.moves[i][0])
moves_array[i * 2 + 1] = black.moves[i][1]
scores[i * 2 + 1] = black.moves[i][2]
return env, fen_data, moves_array, scores, True