def worker(args, pid):
board_size = args.board_size
rounds = args.rounds
max_moves = args.max_moves
temperature = args.temperature
for i in range(args.num_games):
print('Process %d: Generating game %d/%d...' % (pid, i + 1, args.num_games))
game = goboard.GameState.new_game(board_size)
bot = mcts.MCTSAgent(rounds, temperature)
num_moves = 0
sgfgame = sgf.Sgf_game(board_size) # recorder
sgf_info = adaptor.DokiGo_to_SGF() # adaptor
while not game.is_over():
# print_board(game.board)
move = bot.select_move(game)
# print_move(game.next_player, move)
node = sgfgame.extend_main_sequence()
sgf_info.set_move(game.next_player, move) # recorder get info
node.set_move(sgf_info.move_color, sgf_info.move_coordinates) # set node
game = game.apply_move(move)
num_moves += 1
if num_moves > max_moves:
break
sgf_info.set_game_result(game, sgfgame) # set game result
with open(f"./selfplay_data/process{pid}_game{i}_rounds{rounds}.sgf", "wb") as f: # write into a file
f.write(sgfgame.serialise())
def main():
board_size = 9
game = goboard.GameState.new_game(board_size)
#bot = RandomBot()
#bot = MCTSAgent(50,0.8)
model, encoder = load_nn_model(
"dokigo/DL_policy_prediction_MCTS/mcts_r50k.h5")
bot = DeepLearningAgent(model, encoder)
human_player = random.choice(list(base.Player))
print("Game Start! You will play: %s and AI will play: %s" %
(human_player.name, human_player.other.name))
sgfgame = sgf.Sgf_game(board_size) # recorder
sgf_info = adaptor.DokiGo_to_SGF() # adaptor
while not game.is_over():
print(chr(27) + "[2J")
print_board(game.board)
if game.next_player == human_player:
while True:
human_move = input('-- ')
if human_move == 'pass':
move = goboard.Move.pass_turn()
break
elif human_move == 'resign':
move = goboard.Move.resign()
break
else:
point = point_from_coords(human_move.replace(' ', ''))
move = goboard.Move.play(point)
if game.board.is_on_grid(
move.point) and game.is_valid_move(move):
break
print("This is not a value move!")
else:
move = bot.select_move(game)
print_move(game.next_player, move)
game = game.apply_move(move)
node = sgfgame.extend_main_sequence()
sgf_info.set_move(game.next_player, move) # recorder get info
node.set_move(sgf_info.move_color,
sgf_info.move_coordinates) # set node
sgf_info.set_game_result(game, sgfgame) # set game result
with open(f"game_human_vs_bot.sgf", "wb") as f: # write into a file
f.write(sgfgame.serialise())
def main():
board_size = 9
total_games = 1
strong_bot_wins = 0
for m in range(total_games):
game = goboard.GameState.new_game(board_size)
bot_1 = RandomBot()
bot_2 = MCTSAgent(100, 1.5)
bot_1_color = random.choice(list(base.Player))
bot_2_color = bot_1_color.other
sgfgame = sgf.Sgf_game(board_size) #recorder
sgf_info = adaptor.DokiGo_to_SGF() #adaptor
while not game.is_over():
# print(chr(27) + "[2J")
# print_board(game.board)
node = sgfgame.extend_main_sequence()
if game.next_player == bot_1_color:
move = bot_1.select_move(game)
else:
move = bot_2.select_move(game)
#print_move(game.next_player, move)
print("we are in game #:", m + 1)
print("The advanced algorithm win #", strong_bot_wins)
sgf_info.set_move(game.next_player, move) #recorder get info
node.set_move(sgf_info.move_color,
sgf_info.move_coordinates) #set node
game = game.apply_move(move)
if game.winner() == bot_2_color:
strong_bot_wins += 1
sgf_info.set_game_result(game, sgfgame) #set game result
with open(f"game{m}.sgf", "wb") as f: #write into a file
f.write(sgfgame.serialise())
print("The winning rate is %.3f" % (strong_bot_wins / total_games))