def main():
parser = argparse.ArgumentParser()
parser.add_argument('--bind-address', default='127.0.0.1')
parser.add_argument('--port', '-p', type=int, default=5000)
parser.add_argument('--pg-agent')
parser.add_argument('--predict-agent')
parser.add_argument('--q-agent')
parser.add_argument('--ac-agent')
args = parser.parse_args()
bots = {'mcts': mcts.MCTSAgent(800, temperature=0.7)}
if args.pg_agent:
bots['pg'] = agent.load_policy_agent(h5py.File(args.pg_agent))
if args.predict_agent:
bots['predict'] = agent.load_prediction_agent(
h5py.File(args.predict_agent))
if args.q_agent:
q_bot = rl.load_q_agent(h5py.File(args.q_agent))
q_bot.set_temperature(0.01)
bots['q'] = q_bot
if args.ac_agent:
ac_bot = rl.load_ac_agent(h5py.File(args.ac_agent))
ac_bot.set_temperature(0.05)
bots['ac'] = ac_bot
web_app = httpfrontend.get_web_app(bots)
web_app.run(host=args.bind_address, port=args.port, threaded=False)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--num-games', '-g', type=int)
parser.add_argument('--board-size', '-b', type=int)
parser.add_argument('agents', nargs='+')
args = parser.parse_args()
agents = [
# agent.load_policy_agent(h5py.File(filename))
rl.load_q_agent(h5py.File(filename)) for filename in args.agents
]
for a in agents:
a.set_temperature(0.02)
ratings = elo.calculate_ratings(agents, args.num_games, args.board_size)
for filename, rating in zip(args.agents, ratings):
print("%s %d" % (filename, rating))
def main():
workdir = '//home/nail//Code_Go//checkpoints//'
os.chdir(workdir)
bind_address = '127.0.0.1'
port = 5000
predict_agent, pg_agent, q_agent, ac_agent = '', '', '', ''
agent_type = input('Агент(pg/predict/q/ac = ').lower()
if agent_type == 'pg':
pg_agent = input(
'Введите имя файла для игры с ботом политика градиентов =')
pg_agent = workdir + pg_agent + '.h5'
if agent_type == 'predict':
predict_agent = input(
'Введите имя файла для игры с ботом предсказания хода =')
predict_agent = workdir + predict_agent + '.h5'
if agent_type == 'q':
q_agent = input(
'Введите имя файла для игры с ботом ценность действия =')
q_agent = workdir + q_agent + '.h5'
if agent_type == 'ac':
ac_agent = input('Введите имя файла для игры с ботом актор-критик =')
ac_agent = workdir + ac_agent + '.h5'
bots = {'mcts': mcts.MCTSAgent(800, temperature=0.7)}
if agent_type == 'pg':
bots['pg'] = agent.load_policy_agent(h5py.File(pg_agent, 'r'))
if agent_type == 'predict':
bots['predict'] = agent.load_prediction_agent(
h5py.File(predict_agent, 'r'))
if agent_type == 'q':
q_bot = rl.load_q_agent(h5py.File(q_agent, 'r'))
q_bot.set_temperature(0.01)
bots['q'] = q_bot
if agent_type == 'ac':
ac_bot = rl.load_ac_agent(h5py.File(ac_agent, 'r'))
ac_bot.set_temperature(0.05)
bots['ac'] = ac_bot
web_app = httpfrontend.get_web_app(bots)
web_app.run(host=bind_address, port=port, threaded=False)
def load_agent(filename):
with h5py.File(filename, 'r') as h5file:
return rl.load_q_agent(h5file)
def main():
# parser = argparse.ArgumentParser()
# parser.add_argument('--learning-agent', required=True)
# parser.add_argument('--num-games', '-n', type=int, default=10)
# parser.add_argument('--game-log-out', required=True)
# parser.add_argument('--experience-out', required=True)
# parser.add_argument('--temperature', type=float, default=0.0)
learning_agent = input("Бот: ")
temperature = float(input('Температура = '))
game_log = input('game_log: ')
experience_out = input('experience_out: ')
num_games = int(input('Количество игр = '))
board_size = 19
try:
chunk_size = int(input('Количество игр в "порции" ='))
except:
chunk_size = 100
pth = "//home//nail//Experience//"
learning_agent = '//home//nail//Code_Go//checkpoints//' + learning_agent + '.h5'
game_log = pth + game_log + '_' + str(num_games)
experience_out = pth + experience_out + '_' + str(num_games) + '_' #+'.h5'
#args = parser.parse_args()
# ==================================================
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow as tf
config = tf.compat.v1.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.95
config.gpu_options.allow_growth = True
config.log_device_placement = True
sess = tf.compat.v1.Session(config=config)
tf.compat.v1.keras.backend.set_session(sess)
# ==================================================
agent1 = rl.load_q_agent(h5py.File(learning_agent, "r"))
agent2 = rl.load_q_agent(h5py.File(learning_agent, "r"))
agent1.set_temperature(temperature)
agent2.set_temperature(temperature)
k = 0
j = 0
for i in range(num_games + 1):
if j == 0:
game_log_out = game_log + '_' + str((k + 1) * chunk_size) + ".txt"
logf = open(game_log_out, 'a')
logf.write('Начало игр в %s\n' %
(datetime.datetime.now().strftime('%Y-%m-%d %H:%M'), ))
logf.write(
str((k + 1) * chunk_size) + ' из количества игр: ' +
str(num_games) + '\n')
print('Моделируемая игра %d/%d...' % (i + 1, num_games))
collector1 = rl.ExperienceCollector()
collector2 = rl.ExperienceCollector()
color1 = Player.black
j += 1
collector1.begin_episode()
agent1.set_collector(collector1)
collector2.begin_episode()
agent2.set_collector(collector2)
if color1 == Player.black:
black_player, white_player = agent1, agent2
else:
white_player, black_player = agent2, agent1
game_record = simulate_game_q(black_player, white_player, board_size)
print(" № игры : ", i + 1)
if game_record.winner == color1:
print('Агент 1 выигрывает.')
collector1.complete_episode(reward=1)
collector2.complete_episode(reward=-1)
else:
print('Агент 2 выигрывает.')
collector2.complete_episode(reward=1)
collector1.complete_episode(reward=-1)
color1 = color1.other
if i >= chunk_size and i % chunk_size == 0:
experience = rl.combine_experience([collector1, collector2])
experience_out_file = experience_out + str(
(k + 1) * chunk_size) + ".h5"
logf.write('Сохранение буфера в файл %s\n' % experience_out_file)
logf.write('Завершение игр %s\n' %
(datetime.datetime.now().strftime('%Y-%m-%d %H:%M'), ))
logf.close()
with h5py.File(experience_out_file, 'w') as experience_outf:
experience.serialize(experience_outf)
print('Записано игр: ', (k + 1) * chunk_size, ' из ', num_games,
' игр.')
k += 1
j = 0