parser.add_argument('-w',
'--weight',
help='trained weight path',
type=str,
default='')
args = parser.parse_args()
mode = args.mode
cfg_abs_path = parser.parse_args().cfg
cfg = YAML().load(open(cfg_abs_path, 'r'))
# save the configuration and other files
rsg_root = os.path.dirname(os.path.abspath(__file__)) + '/../'
log_dir = rsg_root + '/QuadrotorTrainingdata'
saver = ConfigurationSaver(
log_dir=log_dir + '/quadrotor_position_tracking',
save_items=[
rsg_root + 'raisim_gym/env/env/hummingbird/Environment.hpp',
cfg_abs_path
])
# create environment from the configuration file
if args.mode == "test": # for test mode, force # of env to 1
cfg['environment']['num_envs'] = 1
env = Environment(
RaisimGymEnv(__RSCDIR__, dump(cfg['environment'], Dumper=RoundTripDumper)))
if mode == 'train':
# Get algorithm
model = PPO2(
tensorboard_log=saver.data_dir,
policy=MlpPolicy,
cfg_abs_path = parser.parse_args().cfg
cfg = YAML().load(open(cfg_abs_path, 'r'))
# save the configuration and other files
rsg_root = os.path.dirname(os.path.abspath(__file__)) + '/../'
log_dir = rsg_root + '/data'
# create environment from the configuration file
if args.mode == "test": # for test mode, force # of env to 1
cfg['environment']['num_envs'] = 1
env = Environment(RaisimGymEnv(__RSCDIR__, dump(cfg['environment'], Dumper=RoundTripDumper)))
# Get algorithm
if mode == 'train':
saver = ConfigurationSaver(log_dir=log_dir+'/Jueying_blind_locomotion',
save_items=[rsg_root+'raisim_gym/env/env/jueying/Environment.hpp', cfg_abs_path])
model = PPO2(
tensorboard_log=saver.data_dir,
policy=MlpPolicy,
policy_kwargs=dict(net_arch=[dict(pi=[128, 128], vf=[128, 128])]),
env=env,
gamma=0.998,
n_steps=math.floor(cfg['environment']['max_time'] / cfg['environment']['control_dt']),
ent_coef=0,
learning_rate=1e-3,
vf_coef=0.5,
max_grad_norm=0.5,
lam=0.95,
nminibatches=1,
noptepochs=10,
cliprange=0.2,
default='train')
parser.add_argument('-w',
'--weight',
help='trained weight path',
type=str,
default='')
args = parser.parse_args()
mode = args.mode
cfg_abs_path = parser.parse_args().cfg
cfg = YAML().load(open(cfg_abs_path, 'r'))
# save the configuration and other files
rsg_root = os.path.dirname(os.path.abspath(__file__)) + '/../cartpole'
log_dir = rsg_root + '/data'
saver = ConfigurationSaver(
log_dir=log_dir + '/Cartpole_tutorial',
save_items=[rsg_root + '/Environment.hpp', cfg_abs_path])
# create environment from the configuration file
if args.mode == "test": # for test mode, force # of env to 1
cfg['environment']['num_envs'] = 1
env = Environment(
RaisimGymEnv(current_dir + "/rsc",
dump(cfg['environment'], Dumper=RoundTripDumper)))
if mode == 'train':
# tensorboard, this will open your default browser.
TensorboardLauncher(saver.data_dir + '/PPO2_1')
# Get algorithm
model = PPO2(
tensorboard_log=saver.data_dir,
# configuration
parser = argparse.ArgumentParser()
parser.add_argument('--cfg',
type=str,
default=os.path.abspath(__RSCDIR__ + "/default_cfg.yaml"),
help='configuration file')
cfg_abs_path = parser.parse_args().cfg
cfg = YAML().load(open(cfg_abs_path, 'r'))
# save the configuration and other files
rsg_root = os.path.dirname(os.path.abspath(__file__)) + '/../'
log_dir = rsg_root + '/data'
saver = ConfigurationSaver(
log_dir=log_dir + '/jueying',
save_items=[
rsg_root + 'raisim_gym/env/env/jueying/Environment.hpp', cfg_abs_path
])
# create environment from the configuration file
env = Environment(
RaisimGymEnv(__RSCDIR__, dump(cfg['environment'], Dumper=RoundTripDumper)))
# Get algorithm
model = PPO2(
tensorboard_log=saver.data_dir,
policy=MlpPolicy,
policy_kwargs=dict(net_arch=[dict(pi=[128, 128], vf=[128, 128])]),
env=env,
gamma=0.998,
n_steps=math.floor(cfg['environment']['max_time'] /
CONTINUE_TRAIN = True
# configuration
parser = argparse.ArgumentParser()
parser.add_argument('--cfg', type=str, default=os.path.abspath(__RSCDIR__ + "/default_cfg.yaml"),
help='configuration file')
parser.add_argument('-m', '--mode', help='set mode either train or test', type=str, default='train')
parser.add_argument('-w', '--weight', help='trained weight path', type=str, default='')
args = parser.parse_args()
mode = args.mode
cfg_abs_path = parser.parse_args().cfg
cfg = YAML().load(open(cfg_abs_path, 'r'))
# save the configuration and other files
rsg_root = os.path.dirname(os.path.abspath(__file__))
log_dir = rsg_root + '/data'
saver = ConfigurationSaver(log_dir=log_dir,
save_items=[rsg_root + '/Environment.hpp', cfg_abs_path])
# получить список файлов, отсортировать его по дате, взять путь до последнего)
# create environment from the configuration file
if args.mode == "test": # for test mode, force # of env to 1
cfg['environment']['num_envs'] = 1
env = Environment(RaisimGymEnv(__RSCDIR__, dump(cfg['environment'], Dumper=RoundTripDumper)))
def get_last_weight():
entrys = os.scandir(log_dir)
entrys = reversed(sorted(entrys, key=lambda e: e.stat().st_mtime))
for file in entrys:
if os.path.isfile(file.path) and file.path[-4:]=='.pkl':
print(f"using {file.path} for continue learn")
return file.path
# configuration
parser = argparse.ArgumentParser()
parser.add_argument('--cfg',
type=str,
default=os.path.abspath(__RSCDIR__ + "/train_cfg.yaml"),
help='configuration file')
cfg_abs_path = parser.parse_args().cfg
cfg = YAML().load(open(cfg_abs_path, 'r'))
# save the configuration and other files
rsg_root = os.path.dirname(os.path.abspath(__file__)) + '/../'
log_dir = rsg_root + '/data'
saver = ConfigurationSaver(
log_dir=log_dir + '/spacebok_blind_locomotion',
save_items=[
rsg_root + 'raisim_gym/env/env/spacebok/Environment.hpp', cfg_abs_path
])
# create environment from the configuration file
env = Environment(
RaisimGymEnv(__RSCDIR__, dump(cfg['environment'], Dumper=RoundTripDumper)))
# Get algorithm
model = PPO2(
tensorboard_log=saver.data_dir,
policy=MlpPolicy,
policy_kwargs=dict(net_arch=[dict(pi=[128, 128], vf=[128, 128])]),
env=env,
gamma=0.998,
n_steps=math.floor(cfg['environment']['max_time'] /
