home_path = task_path + "/../../../.."
#argument
test_mode = sys.argv[1] == 'True'
# config
cfg = YAML().load(open(task_path + "/cfg.yaml", 'r'))
curriculum_start = cfg['environment']['curriculum']['curriculum_start']
# create environment from the configuration file
if test_mode:
cfg_tmp = cfg
cfg_tmp['environment']['num_envs'] = 1
env = VecEnv(
rsg_anymal.RaisimGymEnv(
task_path + "/anymal",
dump(cfg_tmp['environment'], Dumper=RoundTripDumper)),
cfg['environment'])
else:
env = VecEnv(
rsg_anymal.RaisimGymEnv(
task_path + "/anymal",
dump(cfg['environment'], Dumper=RoundTripDumper)),
cfg['environment'])
# shortcuts
ob_dim = env.num_obs
act_dim = env.num_acts
# save the configuration and other files
saver = ConfigurationSaver(log_dir=home_path + "/data",
type=str,
default='')
args = parser.parse_args()
# directories
task_path = os.path.dirname(os.path.realpath(__file__))
home_path = task_path + "/../../../../.."
# config
cfg = YAML().load(open(task_path + "/cfg.yaml", 'r'))
# create environment from the configuration file
cfg['environment']['num_envs'] = 1
env = VecEnv(
rsg_anymal.RaisimGymEnv(home_path + "/rsc",
dump(cfg['environment'], Dumper=RoundTripDumper)),
cfg['environment'])
# shortcuts
ob_dim = env.num_obs
act_dim = env.num_acts
weight_path = args.weight
iteration_number = weight_path.rsplit('/', 1)[1].split('_',
1)[1].rsplit('.', 1)[0]
weight_dir = weight_path.rsplit('/', 1)[0] + '/'
if weight_path == "":
print(
"Can't find trained weight, please provide a trained weight with --weight switch\n"
)
type=str,
default='')
args = parser.parse_args()
# directories
task_path = os.path.dirname(os.path.realpath(__file__))
home_path = task_path + "/../../../../.."
# config
cfg = YAML().load(open(task_path + "/cfg.yaml", 'r'))
# create environment from the configuration file
cfg['environment']['num_envs'] = 1
env = VecEnv(
rsg_anymal.RaisimGymEnv(home_path + "/rsc",
dump(cfg['environment'], Dumper=RoundTripDumper)),
cfg['environment'])
# shortcuts
ob_dim = env.num_obs
act_dim = env.num_acts
weight_path = args.weight
if weight_path == "":
print(
"Can't find trained weight, please provide a trained weight with --weight switch\n"
)
else:
print("Loaded weight from {}\n".format(weight_path))
start = time.time()
env.reset()
mode = args.mode
weight_path = args.weight
# check if gpu is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# directories
task_path = os.path.dirname(os.path.realpath(__file__))
home_path = task_path + "/../../../../.."
# config
cfg = YAML().load(open(task_path + "/cfg.yaml", 'r'))
# create environment from the configuration file
env = VecEnv(
rsg_anymal.RaisimGymEnv(home_path + "/rsc",
dump(cfg['environment'], Dumper=RoundTripDumper)),
cfg['environment'])
# shortcuts
ob_dim = env.num_obs
act_dim = env.num_acts
# Training
n_steps = math.floor(cfg['environment']['max_time'] /
cfg['environment']['control_dt'])
total_steps = n_steps * env.num_envs
avg_rewards = []
actor = ppo_module.Actor(
ppo_module.MLP(cfg['architecture']['policy_net'], nn.LeakyReLU, ob_dim,