本文整理汇总了Python中baselines.logger.get_dir方法的典型用法代码示例。如果您正苦于以下问题:Python logger.get_dir方法的具体用法?Python logger.get_dir怎么用?Python logger.get_dir使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类baselines.logger
的用法示例。
在下文中一共展示了logger.get_dir方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: save
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import get_dir [as 别名]
def save(self, path=None):
"""Save model to a pickle located at `path`"""
if path is None:
path = os.path.join(logger.get_dir(), "model.pkl")
with tempfile.TemporaryDirectory() as td:
save_state(os.path.join(td, "model"))
arc_name = os.path.join(td, "packed.zip")
with zipfile.ZipFile(arc_name, 'w') as zipf:
for root, dirs, files in os.walk(td):
for fname in files:
file_path = os.path.join(root, fname)
if file_path != arc_name:
zipf.write(file_path, os.path.relpath(file_path, td))
with open(arc_name, "rb") as f:
model_data = f.read()
with open(path, "wb") as f:
cloudpickle.dump((model_data, self._act_params), f)
示例2: save_act
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import get_dir [as 别名]
def save_act(self, path=None):
"""Save model to a pickle located at `path`"""
if path is None:
path = os.path.join(logger.get_dir(), "model.pkl")
with tempfile.TemporaryDirectory() as td:
save_state(os.path.join(td, "model"))
arc_name = os.path.join(td, "packed.zip")
with zipfile.ZipFile(arc_name, 'w') as zipf:
for root, dirs, files in os.walk(td):
for fname in files:
file_path = os.path.join(root, fname)
if file_path != arc_name:
zipf.write(file_path, os.path.relpath(file_path, td))
with open(arc_name, "rb") as f:
model_data = f.read()
with open(path, "wb") as f:
cloudpickle.dump((model_data, self._act_params), f)
示例3: main
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import get_dir [as 别名]
def main():
logger.configure()
parser = mujoco_arg_parser()
parser.add_argument('--model-path', default=os.path.join(logger.get_dir(), 'humanoid_policy'))
parser.set_defaults(num_timesteps=int(2e7))
args = parser.parse_args()
if not args.play:
# train the model
train(num_timesteps=args.num_timesteps, seed=args.seed, model_path=args.model_path)
else:
# construct the model object, load pre-trained model and render
pi = train(num_timesteps=1, seed=args.seed)
U.load_state(args.model_path)
env = make_mujoco_env('Humanoid-v2', seed=0)
ob = env.reset()
while True:
action = pi.act(stochastic=False, ob=ob)[0]
ob, _, done, _ = env.step(action)
env.render()
if done:
ob = env.reset()
示例4: launch_tensorboard_in_background
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import get_dir [as 别名]
def launch_tensorboard_in_background(log_dir):
'''
To log the Tensorflow graph when using rl-algs
algorithms, you can run the following code
in your main script:
import threading, time
def start_tensorboard(session):
time.sleep(10) # Wait until graph is setup
tb_path = osp.join(logger.get_dir(), 'tb')
summary_writer = tf.summary.FileWriter(tb_path, graph=session.graph)
summary_op = tf.summary.merge_all()
launch_tensorboard_in_background(tb_path)
session = tf.get_default_session()
t = threading.Thread(target=start_tensorboard, args=([session]))
t.start()
'''
import subprocess
subprocess.Popen(['tensorboard', '--logdir', log_dir])
示例5: train
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import get_dir [as 别名]
def train(env_id, num_timesteps, seed):
whoami = mpi_fork(num_cpu)
if whoami == "parent":
return
import baselines.common.tf_util as U
logger.session().__enter__()
sess = U.single_threaded_session()
sess.__enter__()
rank = MPI.COMM_WORLD.Get_rank()
if rank != 0:
logger.set_level(logger.DISABLED)
workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank()
set_global_seeds(workerseed)
env = gym.make(env_id)
def policy_fn(name, ob_space, ac_space):
return MlpPolicy(name=name, ob_space=env.observation_space, ac_space=env.action_space,
hid_size=32, num_hid_layers=2)
env = bench.Monitor(env, osp.join(logger.get_dir(), "%i.monitor.json" % rank))
env.seed(workerseed)
gym.logger.setLevel(logging.WARN)
trpo_mpi.learn(env, policy_fn, timesteps_per_batch=1024, max_kl=0.01, cg_iters=10, cg_damping=0.1,
max_timesteps=num_timesteps, gamma=0.99, lam=0.98, vf_iters=5, vf_stepsize=1e-3)
env.close()
示例6: train
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import get_dir [as 别名]
def train(env_id, num_timesteps, seed):
from baselines.pposgd import mlp_policy, pposgd_simple
U.make_session(num_cpu=1).__enter__()
logger.session().__enter__()
set_global_seeds(seed)
env = gym.make(env_id)
def policy_fn(name, ob_space, ac_space):
return mlp_policy.MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space,
hid_size=64, num_hid_layers=2)
env = bench.Monitor(env, osp.join(logger.get_dir(), "monitor.json"))
env.seed(seed)
gym.logger.setLevel(logging.WARN)
pposgd_simple.learn(env, policy_fn,
max_timesteps=num_timesteps,
timesteps_per_batch=2048,
clip_param=0.2, entcoeff=0.0,
optim_epochs=10, optim_stepsize=3e-4, optim_batchsize=64,
gamma=0.99, lam=0.95,
)
env.close()
示例7: main
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import get_dir [as 别名]
def main():
logger.configure()
env = make_atari('PongNoFrameskip-v4')
env = bench.Monitor(env, logger.get_dir())
env = deepq.wrap_atari_dqn(env)
model = deepq.learn(
env,
"conv_only",
convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)],
hiddens=[256],
dueling=True,
lr=1e-4,
total_timesteps=int(1e7),
buffer_size=10000,
exploration_fraction=0.1,
exploration_final_eps=0.01,
train_freq=4,
learning_starts=10000,
target_network_update_freq=1000,
gamma=0.99,
)
model.save('pong_model.pkl')
env.close()
示例8: save_act
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import get_dir [as 别名]
def save_act(self, path=None):
"""Save model to a pickle located at `path`"""
if path is None:
path = os.path.join(logger.get_dir(), "model.pkl")
with tempfile.TemporaryDirectory() as td:
save_variables(os.path.join(td, "model"))
arc_name = os.path.join(td, "packed.zip")
with zipfile.ZipFile(arc_name, 'w') as zipf:
for root, dirs, files in os.walk(td):
for fname in files:
file_path = os.path.join(root, fname)
if file_path != arc_name:
zipf.write(file_path, os.path.relpath(file_path, td))
with open(arc_name, "rb") as f:
model_data = f.read()
with open(path, "wb") as f:
cloudpickle.dump((model_data, self._act_params), f)
示例9: main
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import get_dir [as 别名]
def main():
logger.configure()
parser = mujoco_arg_parser()
parser.add_argument('--model-path', default=os.path.join(logger.get_dir(), 'humanoid_policy'))
parser.set_defaults(num_timesteps=int(2e7))
args = parser.parse_args()
if not args.play:
# train the model
train(num_timesteps=args.num_timesteps, seed=args.seed, model_path=args.model_path)
else:
# construct the model object, load pre-trained model and render
pi = train(num_timesteps=1, seed=args.seed)
U.load_state(args.model_path)
env = make_mujoco_env('Humanoid-v2', seed=0)
ob = env.reset()
while True:
action = pi.act(stochastic=False, ob=ob)[0]
ob, _, done, _ = env.step(action)
env.render()
if done:
ob = env.reset()
示例10: make_env
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import get_dir [as 别名]
def make_env(env_id, env_type, subrank=0, seed=None, reward_scale=1.0, gamestate=None, wrapper_kwargs={}):
mpi_rank = MPI.COMM_WORLD.Get_rank() if MPI else 0
if env_type == 'atari':
env = make_atari(env_id)
elif env_type == 'retro':
import retro
gamestate = gamestate or retro.State.DEFAULT
env = retro_wrappers.make_retro(game=env_id, max_episode_steps=10000, use_restricted_actions=retro.Actions.DISCRETE, state=gamestate)
else:
env = gym.make(env_id)
env.seed(seed + subrank if seed is not None else None)
env = Monitor(env,
logger.get_dir() and os.path.join(logger.get_dir(), str(mpi_rank) + '.' + str(subrank)),
allow_early_resets=True)
if env_type == 'atari':
env = wrap_deepmind(env, **wrapper_kwargs)
elif env_type == 'retro':
env = retro_wrappers.wrap_deepmind_retro(env, **wrapper_kwargs)
if reward_scale != 1:
env = retro_wrappers.RewardScaler(env, reward_scale)
return env
示例11: make_atari_env
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import get_dir [as 别名]
def make_atari_env(env_id, num_env, seed, hparams=None, wrapper_kwargs=None, start_index=0, nsteps=5, **kwargs):
"""
Create a wrapped, monitored SubprocVecEnv for Atari.
"""
if wrapper_kwargs is None: wrapper_kwargs = {}
def make_env(rank): # pylint: disable=C0111
def _thunk():
env = make_atari(env_id)
env.seed(seed + rank)
env = Monitor(env, logger.get_dir() and os.path.join(logger.get_dir(), str(rank)))
if rank == start_index and 'video_log_dir' in kwargs:
env = VideoLogMonitor(env, kwargs['video_log_dir'] + '_rgb', write_attention_video=kwargs['write_attention_video'], hparams=hparams, nsteps=nsteps)
return wrap_deepmind(env, **wrapper_kwargs)
return _thunk
set_global_seeds(seed)
env_fns = [make_env(i + start_index) for i in range(num_env)]
global my_subproc_vec_env
assert my_subproc_vec_env == None
my_subproc_vec_env = SubprocVecEnv(env_fns)
return my_subproc_vec_env
示例12: main
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import get_dir [as 别名]
def main(args):
U.make_session(num_cpu=1).__enter__()
set_global_seeds(args.seed)
env = gym.make(args.env_id)
def policy_fn(name, ob_space, ac_space, reuse=False):
return mlp_policy.MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space,
reuse=reuse, hid_size=args.policy_hidden_size, num_hid_layers=2)
env = bench.Monitor(env, logger.get_dir() and
osp.join(logger.get_dir(), "monitor.json"))
env.seed(args.seed)
gym.logger.setLevel(logging.WARN)
task_name = get_task_name(args)
args.checkpoint_dir = osp.join(args.checkpoint_dir, task_name)
args.log_dir = osp.join(args.log_dir, task_name)
dataset = Mujoco_Dset(expert_path=args.expert_path, traj_limitation=args.traj_limitation)
savedir_fname = learn(env,
policy_fn,
dataset,
max_iters=args.BC_max_iter,
ckpt_dir=args.checkpoint_dir,
log_dir=args.log_dir,
task_name=task_name,
verbose=True)
avg_len, avg_ret = runner(env,
policy_fn,
savedir_fname,
timesteps_per_batch=1024,
number_trajs=10,
stochastic_policy=args.stochastic_policy,
save=args.save_sample,
reuse=True)
示例13: main
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import get_dir [as 别名]
def main():
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--env', help='environment ID', default='BreakoutNoFrameskip-v4')
parser.add_argument('--seed', help='RNG seed', type=int, default=0)
parser.add_argument('--prioritized', type=int, default=1)
parser.add_argument('--dueling', type=int, default=1)
parser.add_argument('--num-timesteps', type=int, default=int(10e6))
args = parser.parse_args()
logger.configure()
set_global_seeds(args.seed)
env = make_atari(args.env)
env = bench.Monitor(env, logger.get_dir())
env = deepq.wrap_atari_dqn(env)
model = deepq.models.cnn_to_mlp(
convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)],
hiddens=[256],
dueling=bool(args.dueling),
)
act = deepq.learn(
env,
q_func=model,
lr=1e-4,
max_timesteps=args.num_timesteps,
buffer_size=10000,
exploration_fraction=0.1,
exploration_final_eps=0.01,
train_freq=4,
learning_starts=10000,
target_network_update_freq=1000,
gamma=0.99,
prioritized_replay=bool(args.prioritized)
)
# act.save("pong_model.pkl") XXX
env.close()
示例14: train
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import get_dir [as 别名]
def train(env_id, num_timesteps, seed, policy, r_ex_coef, r_in_coef, lr_alpha, lr_beta, reward_freq):
from baselines.common import set_global_seeds
from baselines.common.vec_env.vec_normalize import VecNormalize
from baselines.ppo2 import ppo2
from baselines.ppo2.policies import MlpPolicy, MlpPolicyIntrinsicReward
import gym
import tensorflow as tf
from baselines.common.vec_env.dummy_vec_env import DummyVecEnv
ncpu = 1
config = tf.ConfigProto(allow_soft_placement=True,
intra_op_parallelism_threads=ncpu,
inter_op_parallelism_threads=ncpu)
config.gpu_options.allow_growth = True
tf.Session(config=config).__enter__()
def make_env():
env = gym.make(env_id)
env = bench.Monitor(env, logger.get_dir())
return env
env = DummyVecEnv([make_env])
env = VecNormalize(env)
set_global_seeds(seed)
if policy == 'mlp':
policy = MlpPolicy
elif policy == 'mlp_int':
policy = MlpPolicyIntrinsicReward
else:
raise NotImplementedError
ppo2.learn(policy=policy, env=env, nsteps=2048, nminibatches=32,
lam=0.95, gamma=0.99, noptepochs=10, log_interval=1,
ent_coef=0.0,
lr_alpha=lr_alpha,
cliprange=0.2,
total_timesteps=num_timesteps,
r_ex_coef=r_ex_coef,
r_in_coef=r_in_coef,
lr_beta=lr_beta,
reward_freq=reward_freq)
示例15: train
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import get_dir [as 别名]
def train(env_id, num_timesteps, seed):
from baselines.ppo1 import pposgd_simple, cnn_policy
import baselines.common.tf_util as U
rank = MPI.COMM_WORLD.Get_rank()
sess = U.single_threaded_session()
sess.__enter__()
if rank == 0:
logger.configure()
else:
logger.configure(format_strs=[])
workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank()
set_global_seeds(workerseed)
env = make_atari(env_id)
def policy_fn(name, ob_space, ac_space): #pylint: disable=W0613
return cnn_policy.CnnPolicy(name=name, ob_space=ob_space, ac_space=ac_space)
env = bench.Monitor(env, logger.get_dir() and
osp.join(logger.get_dir(), str(rank)))
env.seed(workerseed)
env = wrap_deepmind(env)
env.seed(workerseed)
pposgd_simple.learn(env, policy_fn,
max_timesteps=int(num_timesteps * 1.1),
timesteps_per_actorbatch=256,
clip_param=0.2, entcoeff=0.01,
optim_epochs=4, optim_stepsize=1e-3, optim_batchsize=64,
gamma=0.99, lam=0.95,
schedule='linear'
)
env.close()