def main():
args = get_args()
nn.set_default_context(
get_extension_context(args.extension, device_id=args.device_id))
from atari_utils import make_atari_deepmind
env = make_atari_deepmind(args.gym_env, valid=True)
print('Observation:', env.observation_space)
print('Action:', env.action_space)
obs_sampler = ObsSampler(args.num_frames)
val_replay_memory = ReplayMemory(env.observation_space.shape,
env.action_space.shape,
max_memory=args.num_frames)
# for one file
explorer = GreedyExplorer(env.action_space.n,
use_nnp=True,
nnp_file=args.nnp,
name='qnet')
validator = Validator(env,
val_replay_memory,
explorer,
obs_sampler,
num_episodes=30,
clip_episode_step=True,
render=not args.no_render)
mean_reward = validator.step()
with open(os.path.join(args.log_path, 'mean_reward.txt'), 'a') as f:
print("{} {}".format(args.gym_env, str(mean_reward)), file=f)
def main():
args = get_args()
nn.set_default_context(
get_extension_context(args.extension, device_id=args.device_id))
if args.nnp is None:
local_nnp_dir = os.path.join("asset", args.gym_env)
local_nnp_file = os.path.join(local_nnp_dir, "qnet.nnp")
if not find_local_nnp(args.gym_env):
logger.info("Downloading nnp data since you didn't specify...")
nnp_uri = os.path.join(
"https://nnabla.org/pretrained-models/nnp_models/examples/dqn",
args.gym_env, "qnet.nnp")
if not os.path.exists(local_nnp_dir):
os.mkdir(local_nnp_dir)
download(nnp_uri, output_file=local_nnp_file, open_file=False)
logger.info("Download done!")
args.nnp = local_nnp_file
from atari_utils import make_atari_deepmind
env = make_atari_deepmind(args.gym_env, valid=False)
print('Observation:', env.observation_space)
print('Action:', env.action_space)
obs_sampler = ObsSampler(args.num_frames)
val_replay_memory = ReplayMemory(env.observation_space.shape,
env.action_space.shape,
max_memory=args.num_frames)
# just play greedily
explorer = GreedyExplorer(env.action_space.n,
use_nnp=True,
nnp_file=args.nnp,
name='qnet')
validator = Validator(env,
val_replay_memory,
explorer,
obs_sampler,
num_episodes=1,
render=not args.no_render)
while True:
validator.step()
def main():
args = get_args()
device = torch.device('cuda',
index=args.device_id) if torch.cuda.is_available(
) else torch.device('cpu')
if torch.cuda.is_available():
torch.cuda.set_device(args.device_id)
if args.log_path:
output_path = OutputPath(args.log_path)
else:
output_path = OutputPath()
# monitor = Monitor(output_path.path)
tbw = SummaryWriter(output_path.path)
# Create an atari env.
from atari_utils import make_atari_deepmind
env = make_atari_deepmind(args.gym_env, valid=False)
env_val = make_atari_deepmind(args.gym_env, valid=True)
print('Observation:', env.observation_space)
print('Action:', env.action_space)
# 10000 * 4 frames
val_replay_memory = ReplayMemory(env.observation_space.shape,
env.action_space.shape,
max_memory=args.num_frames)
replay_memory = ReplayMemory(env.observation_space.shape,
env.action_space.shape,
max_memory=40000)
learner = QLearner(env.action_space.n,
device,
sync_freq=1000,
save_freq=250000,
gamma=0.99,
learning_rate=1e-4,
save_path=output_path)
explorer = LinearDecayEGreedyExplorer(env.action_space.n,
device,
network=learner.get_network(),
eps_start=1.0,
eps_end=0.01,
eps_steps=1e6)
sampler = Sampler(args.num_frames)
obs_sampler = ObsSampler(args.num_frames)
validator = Validator(env_val,
val_replay_memory,
explorer,
obs_sampler,
num_episodes=args.num_val_episodes,
num_eval_steps=args.num_eval_steps,
render=args.render_val,
tbw=tbw)
trainer_with_validator = Trainer(env,
replay_memory,
learner,
sampler,
explorer,
obs_sampler,
inter_eval_steps=args.inter_eval_steps,
num_episodes=args.num_episodes,
train_start=10000,
batch_size=32,
render=args.render_train,
validator=validator,
tbw=tbw)
for e in range(args.num_epochs):
trainer_with_validator.step()
def main():
args = get_args()
nn.set_default_context(
get_extension_context(args.extension, device_id=args.device_id))
if args.log_path:
output_path = OutputPath(args.log_path)
else:
output_path = OutputPath()
monitor = Monitor(output_path.path)
tbw = SummaryWriter(output_path.path)
# Create an atari env.
from atari_utils import make_atari_deepmind
env = make_atari_deepmind(args.gym_env, valid=False)
env_val = make_atari_deepmind(args.gym_env, valid=True)
print('Observation:', env.observation_space)
print('Action:', env.action_space)
# 10000 * 4 frames
val_replay_memory = ReplayMemory(env.observation_space.shape,
env.action_space.shape,
max_memory=args.num_frames)
replay_memory = ReplayMemory(env.observation_space.shape,
env.action_space.shape,
max_memory=40000)
learner = QLearner(q_cnn,
env.action_space.n,
sync_freq=1000,
save_freq=250000,
gamma=0.99,
learning_rate=1e-4,
name_q='q',
save_path=output_path)
explorer = LinearDecayEGreedyExplorer(env.action_space.n,
eps_start=1.0,
eps_end=0.01,
eps_steps=1e6,
q_builder=q_cnn,
name='q')
sampler = Sampler(args.num_frames)
obs_sampler = ObsSampler(args.num_frames)
validator = Validator(env_val,
val_replay_memory,
explorer,
obs_sampler,
num_episodes=args.num_val_episodes,
num_eval_steps=args.num_eval_steps,
render=args.render_val,
monitor=monitor,
tbw=tbw)
trainer_with_validator = Trainer(env,
replay_memory,
learner,
sampler,
explorer,
obs_sampler,
inter_eval_steps=args.inter_eval_steps,
num_episodes=args.num_episodes,
train_start=10000,
batch_size=32,
render=args.render_train,
validator=validator,
monitor=monitor,
tbw=tbw)
for e in range(args.num_epochs):
trainer_with_validator.step()