def main():
parser = argparse.ArgumentParser(description='SLPolicyNetwork', formatter_class=RawTextHelpFormatter)
parser.add_argument('CONFIG', default=None, type=str, help='path to config file')
parser.add_argument('MODEL', default=None, type=str, help='path to model.npz')
parser.add_argument('--gpu', type=int, default=-1, help='gpu numbers\nto specify')
parser.add_argument('--debug', default=False, action='store_true', help='switch to debug mode')
args = parser.parse_args()
print('*** load config ***')
with open(args.CONFIG, "r") as f:
config = json.load(f)
print('*** set up model ***')
n_input_channel = config["arguments"]["n_input_channel"]
n_output_channel = config["arguments"]["n_output_channel"]
rl_policy_network = RLPolicyNetwork(n_input_channel=n_input_channel, n_output_channel=n_output_channel)
serializers.load_npz(args.MODEL, rl_policy_network)
optimizer = chainer.optimizers.Adam(alpha=config["arguments"]["learning_rate"])
optimizer.setup(rl_policy_network)
if args.gpu:
cuda.get_device_from_id(args.gpu).use()
rl_policy_network.to_gpu(args.gpu)
xp = cuda.cupy
else:
xp = np
rl_policy_network.set_cache()
# define parameters
N = 30000
batch_size = 150
first_choices = [0x0000100000000000, 0x0000002000000000, 0x0000000004000000, 0x0000000000080000]
bar = ProgressBar(0, N)
print('*** start iteration ***')
for i in range(N):
bar.update(i)
start = time.time()
opponent = SLPolicyNetwork(n_input_channel=n_input_channel, n_output_channel=n_output_channel)
opponent_model_path = np.random.choice(glob.glob("./result/sl_policy/slpn.epoch*.npz"))
print(f'\nmodel:{opponent_model_path} is chosen')
serializers.load_npz(opponent_model_path, opponent)
if args.gpu:
opponent.to_gpu(args.gpu)
opponent.set_cache()
agent = Agent(batch_size, xp, rl_policy_network, optimizer)
env = Env(batch_size, xp, rl_policy_network, opponent)
env.reset()
is_black = True
if i % 2 == 1:
first_actions = xp.random.choice(first_choices, batch_size).astype('uint64').reshape(-1, 1)
reversible_mask = env.reversible(first_actions, is_black)
env.black, env.white = \
env.reverse(first_actions, is_black, reversible_mask)
is_black = not is_black
obs = env.create_current_states(is_black)
done = False
while not done:
action_indices = agent.act(obs)
obs, _, done, _ = env.step(action_indices, is_black)
bs = xp.sum(obs[:, 0].reshape(batch_size, -1), axis=1) # (b, 8, 8) -> (b, )
ws = xp.sum(obs[:, 1].reshape(batch_size, -1), axis=1)
true_rewards = bs > ws if is_black else ws > bs
agent.update(true_rewards)
count = xp.sum(bs > ws) if is_black else xp.sum(ws > bs)
print(f'{time.time() - start:.02f} sec elapsed')
print(f'win rate:{int(count) * 100 / batch_size:.02f}')
else:
serializers.save_npz("result/rl_policy.npz", rl_policy_network)
