def train():
config = Config()
while True:
print("NEW GAME")
winner = None
mcts = MCTS(config)
train_configs = []
target_policys = []
target_values = []
while winner == None:
mcts.search(config)
policy, best_move = mcts.get_mcts_policy()
prepare_training(config, policy, train_configs, target_policys,
target_values)
config.apply_move(best_move, True)
show(config)
winner, _ = config.winner()
objgraph.show_most_common_types()
target_values = np.array(target_values) * winner
train_configs = np.array(train_configs)
target_policys = np.array(target_policys)
mcts.model.fit(train_configs, [target_values, target_policys],
epochs=1,
batch_size=4,
shuffle=True)
mcts.model.save("bot/chess_model.h5")
config.reset()
def play():
config = Config()
winner = None
while winner == None:
show(config)
start, dest = ask()
if config.try_play(start, dest):
winner, _ = config.winner()
else:
print("Invalid move, please replay")
show(config)
show_winner(winner)
def test():
config = Config()
with open('chess/engine/games.csv', newline='') as games:
reader = csv.DictReader(games)
nb = 0
for row in reader:
nb += 1
moves = row['moves'].split()
i = 0
over = None
if row['victory_status'] == 'mate' and nb > 0:
while over == None:
start, dest, prom_not_queen = ask_test(moves[i])
# auto promotion in queen so others are not handled
if prom_not_queen:
over = 0
elif config.try_play(start,dest):
# show(config)
over,_ = config.winner()
else:
sys.exit("fail at move : " + moves[i])
i += 1
config.reset()
