def main():
parser = argparse.ArgumentParser()
parser.add_argument('--agent1', required=True)
parser.add_argument('--agent2', required=True)
parser.add_argument('--num-games', '-n', type=int, default=10)
args = parser.parse_args()
agent1 = rl.load_ac_agent(h5py.File(args.agent1))
agent2 = rl.load_ac_agent(h5py.File(args.agent2))
num_games = args.num_games
wins = 0
losses = 0
color1 = Player.black
for i in range(num_games):
print('Simulating game %d/%d...' % (i + 1, num_games))
if color1 == Player.black:
black_player, white_player = agent1, agent2
else:
white_player, black_player = agent1, agent2
game_record = simulate_game(black_player, white_player)
if game_record.winner == color1:
wins += 1
else:
losses += 1
color1 = color1.other
print('Agent 1 record: %d/%d' % (wins, wins + losses))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--board-size', type=int, default=19)
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)
args = parser.parse_args()
global BOARD_SIZE
BOARD_SIZE = args.board_size
agent1 = rl.load_ac_agent(h5py.File(args.learning_agent))
agent2 = rl.load_ac_agent(h5py.File(args.learning_agent))
agent1.set_temperature(args.temperature)
agent2.set_temperature(args.temperature)
collector1 = rl.ExperienceCollector()
collector2 = rl.ExperienceCollector()
color1 = Player.black
# logf = open(args.game_log_out, 'a')
# logf.write('Begin training at %s\n' % (datetime.datetime.now().strftime('%Y-%m-%d %H:%M'),))
for i in range(args.num_games):
print('Simulating game %d/%d...' % (i + 1, args.num_games))
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 = agent1, agent2
game_record = simulate_game(black_player, white_player)
if game_record.winner == color1:
print('Agent 1 wins.')
collector1.complete_episode(reward=1)
collector2.complete_episode(reward=-1)
else:
print('Agent 2 wins.')
collector2.complete_episode(reward=1)
collector1.complete_episode(reward=-1)
color1 = color1.other
experience = rl.combine_experience([collector1, collector2])
# logf.write('Saving experience buffer to %s\n' % args.experience_out)
with h5py.File(args.experience_out, 'w') as experience_outf:
experience.serialize(experience_outf)
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('--learning-agent', required=True)
parser.add_argument('--agent-out', required=True)
parser.add_argument('--lr', type=float, default=0.0001)
parser.add_argument('--bs', type=int, default=512)
parser.add_argument('experience', nargs='+')
args = parser.parse_args()
learning_agent_filename = args.learning_agent
experience_files = args.experience
updated_agent_filename = args.agent_out
learning_rate = args.lr
batch_size = args.bs
learning_agent = rl.load_ac_agent(h5py.File(learning_agent_filename))
for exp_filename in experience_files:
exp_buffer = rl.load_experience(h5py.File(exp_filename))
learning_agent.train(
exp_buffer,
lr=learning_rate,
batch_size=batch_size)
with h5py.File(updated_agent_filename, 'w') as updated_agent_outf:
learning_agent.serialize(updated_agent_outf)
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_ac_agent(h5file)