def main():
args = parse_args()
set_seed(args.seed)
env = gym.envs.make(args.env_id)
net = get_net(env)
approximator = Approximator(net, alpha=args.alpha, loss=nn.MSELoss)
get_eps = get_get_epsilon(args.it_at_min, args.min_epsilon)
train(approximator, env, get_epsilon=get_eps, **vars(args))
canvasSize = 800
## config parameters
# whether or not to display point-cloud
disp_cloud = False
# tracker
TRACKING = True
# write frames to file
WRITE_TO_FILE = False
OUT_DIR = 'tmp/vr3dense_demo'
# main function
if __name__ == "__main__":
# parse arguments
args = parse_args()
# create an instance of tracker
mot_tracker = AB3DMOT(max_age=2, min_hits=2)
# experiment string
exp_id = 'None'
if args.exp_id != '':
exp_id = args.exp_id
exp_str = 'vr3d.learning_rate_{}.n_xgrids_{}.n_ygrids_{}.xlim_{}_{}.ylim_{}_{}.zlim_{}_{}.max_depth_{}.vol_size_{}x{}x{}.img_size_{}x{}.dense_depth_{}.concat_latent_vector_{}.exp_id_{}'.format(
args.learning_rate, args.n_xgrids, args.n_ygrids, args.xmin, args.xmax, args.ymin, args.ymax, \
args.zmin, args.zmax, args.max_depth, args.vol_size_x, args.vol_size_y, args.vol_size_z, args.img_size_x, \
args.img_size_y, args.dense_depth, args.concat_latent_vector, exp_id)
# define model
obj_label_len = len(pose_fields) + len(
import sys
import src as app
if __name__ == '__main__':
initial_time, range_days, companies = app.parse_args(sys.argv[1:])
allowed_times = app.get_weekdays_for_scheduling(range_days, initial_time)
schedule = app.scheduler(companies, allowed_times)
for s in schedule:
print("{}\n".format(s))