def main(game_name, lr, num_agents, update_target_every, model_name, tau):
assert 'NoFrameskip-v4' in game_name
if 'soft' in model_name:
update_target_every = 1
basename = '{}:lr={}:na={}:ute={}:{}'.format(
game_name[:-14], lr, num_agents, update_target_every, model_name)
if 'soft' in model_name:
basename += ':tau={}'.format(tau)
env = Agent(num_agents, game_name, basename)
try:
estimator = get_estimator(model_name, env.action_n, lr, 0.99, tau=tau)
base_path = os.path.join(train_path, basename)
print("start training!!")
dqn(env,
estimator,
base_path,
batch_size=32,
epsilon=0.01,
save_model_every=1000,
update_target_every=update_target_every,
learning_starts=200,
memory_size=100000,
num_iterations=40000000)
except KeyboardInterrupt:
print("\nKeyboard interrupt!!")
except Exception:
traceback.print_exc()
finally:
env.close()
def main(game_name, lr, num_agents, update_target_every, model_name):
assert 'NoFrameskip-v4' in game_name
basename = '{}:lr={}:na={}:ute={}:{}'.format(game_name[:-14], lr,
num_agents,
update_target_every,
model_name)
env = Agent(num_agents, game_name, basename)
try:
estimator = get_estimator(model_name, env.action_n, lr, 0.99)
base_path = os.path.join(train_path, basename)
print("start training!!")
dqn(env,
estimator,
base_path,
batch_size=32,
epsilon=0.01,
save_model_every=1000,
update_target_every=update_target_every,
learning_starts=200,
memory_size=100000,
num_iterations=40000000)
except KeyboardInterrupt:
print("\nKeyboard interrupt!!")
except Exception:
traceback.print_exc()
finally:
env.close()
from estimator import get_estimator
from data import get_input_fn
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description='training script')
parser.add_argument('model_name', nargs='?', type=str, default='base')
parser.add_argument('--batch_size', '-b', nargs='?', type=int, default=64)
parser.add_argument('--max_steps', '-s', nargs='?', type=int, default=1e6)
args = parser.parse_args()
estimator = get_estimator(args.model_name)
input_fn = get_input_fn(args.batch_size, shuffle=True)
estimator.train(input_fn, max_steps=args.max_steps)
def main(game_name, model_name, write_video):
assert 'NoFrameskip-v4' in game_name
env = atari_env(game_name)
estimator = get_estimator(model_name, env.action_space.n, 0.001, 0.99)
basename_list = [
name for name in os.listdir(train_path)
if (game_name[:-14] in name) and (model_name in name)
]
print(basename_list)
def visualize(basename):
checkpoint_path = os.path.join(train_path, basename, 'models')
estimator.load_model(checkpoint_path)
total_t = estimator.get_global_step()
if not os.path.exists('./videos'):
os.makedirs('./videos')
videoWriter = imageio.get_writer('./videos/{}-{}.mp4'.format(
basename, total_t),
fps=30)
state = env.reset(videowriter=videoWriter)
lives = env.unwrapped.ale.lives()
print(lives)
r = 0
tot = 0
while True:
action = estimator.get_action(np.array([state]), 0.0)
state, reward, done, info = env.step(action)
r += reward
tot += 1
if done:
lives = env.unwrapped.ale.lives()
print(lives)
if info['was_real_done']:
print(tot, r)
break
else:
state = env.reset()
videoWriter.close()
def evaluate(basename, num_eval=10):
checkpoint_path = os.path.join(train_path, basename, 'models')
estimator.load_model(checkpoint_path)
res = []
for i in tqdm(range(num_eval)):
env.seed(int(time.time() * 1000) // 2147483647)
state = env.reset()
r = 0
while True:
action = estimator.get_action(np.array([state]), 0.0)
state, reward, done, info = env.step(action)
r += reward
if done:
if info['was_real_done']:
res.append(r)
break
else:
state = env.reset()
print('mean: {}, max: {}'.format(sum(res) / num_eval, max(res)))
if write_video:
for basename in basename_list:
print("Writing {}'s video ...".format(basename))
visualize(basename)
else:
for basename in basename_list:
print("Evaluating {} ...".format(basename))
evaluate(basename)