本文整理汇总了Python中baselines.logger.configure方法的典型用法代码示例。如果您正苦于以下问题:Python logger.configure方法的具体用法?Python logger.configure怎么用?Python logger.configure使用的例子?那么恭喜您, 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.configure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: main
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import configure [as 别名]
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--env', help='Environment ID', default='BreakoutNoFrameskip-v4')
parser.add_argument('--seed', help='RNG seed', type=int, default=0)
parser.add_argument('--policy', help='Policy architecture', choices=['cnn', 'lstm', 'lnlstm', 'cnn_int'], default='cnn_int')
parser.add_argument('--lrschedule', help='Learning rate schedule', choices=['constant', 'linear'], default='linear')
parser.add_argument('--num-timesteps', type=int, default=int(50E6))
parser.add_argument('--v-ex-coef', type=float, default=0.1)
parser.add_argument('--r-ex-coef', type=float, default=1)
parser.add_argument('--r-in-coef', type=float, default=0.01)
parser.add_argument('--lr-alpha', type=float, default=7E-4)
parser.add_argument('--lr-beta', type=float, default=7E-4)
args = parser.parse_args()
logger.configure()
train(args.env, num_timesteps=args.num_timesteps, seed=args.seed,
policy=args.policy, lrschedule=args.lrschedule, num_env=16,
v_ex_coef=args.v_ex_coef, r_ex_coef=args.r_ex_coef, r_in_coef=args.r_in_coef,
lr_alpha=args.lr_alpha, lr_beta=args.lr_beta)
示例2: main
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import configure [as 别名]
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--env', help='Environment ID', default='Walker2d-v2')
parser.add_argument('--seed', help='RNG seed', type=int, default=0)
parser.add_argument('--policy', help='Policy architecture', choices=['mlp', 'mlp_int'], default='mlp_int')
parser.add_argument('--num-timesteps', type=int, default=int(1E6))
parser.add_argument('--r-ex-coef', type=float, default=0)
parser.add_argument('--r-in-coef', type=float, default=1)
parser.add_argument('--lr-alpha', type=float, default=3E-4)
parser.add_argument('--lr-beta', type=float, default=1E-4)
parser.add_argument('--reward-freq', type=int, default=20)
args = parser.parse_args()
logger.configure()
train(args.env, num_timesteps=args.num_timesteps, seed=args.seed, policy=args.policy,
r_ex_coef=args.r_ex_coef, r_in_coef=args.r_in_coef,
lr_alpha=args.lr_alpha, lr_beta=args.lr_beta,
reward_freq=args.reward_freq)
示例3: train
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import configure [as 别名]
def train(env_id, num_timesteps, seed):
import baselines.common.tf_util as U
sess = U.single_threaded_session()
sess.__enter__()
rank = MPI.COMM_WORLD.Get_rank()
if rank == 0:
logger.configure()
else:
logger.configure(format_strs=[])
logger.set_level(logger.DISABLED)
workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank()
def policy_fn(name, ob_space, ac_space):
return MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space,
hid_size=32, num_hid_layers=2)
env = make_mujoco_env(env_id, workerseed)
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4: main
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import configure [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5: main
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import configure [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6: main
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import configure [as 别名]
def main():
import argparse
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--feature_type', type=str, default='sensor')
parser.add_argument('--tcn_run_idx', type=int, default=1)
parser.add_argument('--split_idx', type=int, default=1)
parser.add_argument('--run_idx', type=int, default=1)
args = parser.parse_args()
logger.configure()
rng_seed = randint(0, 1000)
print(rng_seed)
if args.feature_type not in ['sensor', 'visual']:
raise Exception('Invalid Feature Type')
train(seed=rng_seed,
feature_type=args.feature_type,
tcn_run_idx=args.tcn_run_idx,
split_idx=args.split_idx,
run_idx=args.run_idx)
示例7: main
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import configure [as 别名]
def main():
parser = mujoco_arg_parser()
parser.add_argument('--lr', type=float, default=3e-4, help="Learning rate")
parser.add_argument('--sil-update', type=float, default=10, help="Number of updates per iteration")
parser.add_argument('--sil-value', type=float, default=0.01, help="Weight for value update")
parser.add_argument('--sil-alpha', type=float, default=0.6, help="Alpha for prioritized replay")
parser.add_argument('--sil-beta', type=float, default=0.1, help="Beta for prioritized replay")
args = parser.parse_args()
logger.configure()
model, env = train(args.env, num_timesteps=args.num_timesteps, seed=args.seed,
lr=args.lr,
sil_update=args.sil_update, sil_value=args.sil_value,
sil_alpha=args.sil_alpha, sil_beta=args.sil_beta)
if args.play:
logger.log("Running trained model")
obs = np.zeros((env.num_envs,) + env.observation_space.shape)
obs[:] = env.reset()
while True:
actions = model.step(obs)[0]
obs[:] = env.step(actions)[0]
env.render()
示例8: main
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import configure [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9: main
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import configure [as 别名]
def main():
args = atari_arg_parser().parse_args()
logger.configure()
train(args.env, num_timesteps=args.num_timesteps, seed=args.seed, num_cpu=32)
示例10: main
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import configure [as 别名]
def main():
args = mujoco_arg_parser().parse_args()
logger.configure()
train(args.env, num_timesteps=args.num_timesteps, seed=args.seed)
示例11: train
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import configure [as 别名]
def train(env_id, num_timesteps, seed):
from baselines.trpo_mpi.nosharing_cnn_policy import CnnPolicy
from baselines.trpo_mpi import trpo_mpi
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 CnnPolicy(name=name, ob_space=env.observation_space, ac_space=env.action_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)
trpo_mpi.learn(env, policy_fn, timesteps_per_batch=512, max_kl=0.001, cg_iters=10, cg_damping=1e-3,
max_timesteps=int(num_timesteps * 1.1), gamma=0.98, lam=1.0, vf_iters=3, vf_stepsize=1e-4, entcoeff=0.00)
env.close()
示例12: main
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import configure [as 别名]
def main():
parser = atari_arg_parser()
parser.add_argument('--policy', help='Policy architecture', choices=['cnn', 'lstm', 'lnlstm'], default='cnn')
args = parser.parse_args()
logger.configure()
train(args.env, num_timesteps=args.num_timesteps, seed=args.seed,
policy=args.policy)
示例13: train
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import configure [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()
示例14: main
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import configure [as 别名]
def main():
parser = atari_arg_parser()
parser.add_argument('--policy', help='Policy architecture', choices=['cnn', 'lstm', 'lnlstm'], default='cnn')
parser.add_argument('--lrschedule', help='Learning rate schedule', choices=['constant', 'linear'], default='constant')
parser.add_argument('--logdir', help ='Directory for logging')
args = parser.parse_args()
logger.configure(args.logdir)
train(args.env, num_timesteps=args.num_timesteps, seed=args.seed,
policy=args.policy, lrschedule=args.lrschedule, num_cpu=16)
示例15: main
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import configure [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('--prioritized-replay-alpha', type=float, default=0.6)
parser.add_argument('--dueling', type=int, default=1)
parser.add_argument('--num-timesteps', type=int, default=int(10e6))
parser.add_argument('--checkpoint-freq', type=int, default=10000)
parser.add_argument('--checkpoint-path', type=str, default=None)
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)
deepq.learn(
env,
"conv_only",
convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)],
hiddens=[256],
dueling=bool(args.dueling),
lr=1e-4,
total_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),
prioritized_replay_alpha=args.prioritized_replay_alpha,
checkpoint_freq=args.checkpoint_freq,
checkpoint_path=args.checkpoint_path,
)
env.close()