def main():
env = ttenv.make(
args.env,
render=args.render,
record=args.record,
ros=args.ros,
map_name=args.map,
directory=args.log_dir,
num_targets=args.nb_targets,
is_training=False,
im_size=args.im_size,
)
np.random.seed(args.seed)
for _ in range(args.repeat):
nlogdetcov = []
obs, done = env.reset(), False
while (not done):
if args.render:
env.render()
obs, rew, done, info = env.step(env.action_space.sample())
nlogdetcov.append(info['mean_nlogdetcov'])
print(
"Sum of negative logdet of the target belief covariances : %.2f" %
np.sum(nlogdetcov))
def make(env_name, type, render=False, record=False, directory='', **kwargs):
"""
env_name : str
name of an environment. (e.g. 'Cartpole-v0')
type : str
type of an environment. One of ['atari', 'classic_control',
'classic_mdp','target_tracking']
"""
if type == 'atari':
from baselines0.common.atari_wrappers import make_atari
from baselines0.common.atari_wrappers import wrap_deepmind
from baselines0 import bench, logger
env = make_atari(env_name)
env = bench.Monitor(env, logger.get_dir())
env = wrap_deepmind(env, frame_stack=True, scale=True)
if record:
env = Monitor(env, directory=directory)
elif type == 'classic_control':
env = gym.make(env_name)
if record:
env = Monitor(env, directory=directory)
elif type == 'classic_mdp':
from envs import classic_mdp
env = classic_mdp.model_assign(env_name)
elif type == 'target_tracking':
import ttenv
env = ttenv.make(env_name,
render=render,
record=record,
directory=directory,
**kwargs)
elif type == 'ma_target_tracking':
import maTTenv
env = maTTenv.make(env_name,
render=render,
record=record,
directory=directory,
**kwargs)
else:
raise ValueError('Designate the right type of the environment.')
return env
'_'.join([args.env,
datetime.datetime.now().strftime("%m%d%H%M")]))
if not os.path.exists(logdir):
os.makedirs(logdir)
else:
ValueError("The directory already exists...", logdir)
json.dump(vars(args), open(os.path.join(logdir, 'test_prop.json'), 'w'))
planner = InfoPlanner(n_controls=args.n_controls)
for eval_id in range(args.eval_id_max):
print("\nEvaluating for EVAL ID %d" % eval_id)
env = ttenv.make(args.env,
render=bool(args.render),
record=bool(args.record),
ros=bool(args.ros),
directory=logdir,
map_name=args.map,
num_targets=args.nb_targets,
is_training=False)
env_core = env
while (not hasattr(env_core, '_elapsed_steps')):
env_core = env_core.env
env_core = env_core.env
num_target_dep_vars = env_core.num_target_dep_vars
init_params = {}
given_init_pose, test_init_pose = [], []
nb_test_episodes = args.repeat