from utils import make_var
parser = argparse.ArgumentParser()
parser.add_argument('--env-name', default='SimpleSim-v0')
parser.add_argument('--map-name', required=True)
parser.add_argument('--no-random', action='store_true', help='disable domain randomization')
parser.add_argument('--no-pause', action='store_true', help="don't pause on failure")
args = parser.parse_args()
if args.env_name == 'SimpleSim-v0':
env = DuckietownEnv(
map_name = args.map_name,
domain_rand = not args.no_random
)
#env.max_steps = math.inf
env.max_steps = 500
else:
env = gym.make(args.env_name)
obs = env.reset()
env.render()
avg_frame_time = 0
max_frame_time = 0
def load_model():
global model
model = Model()
try:
state_dict = torch.load('trained_models/imitate.pt', map_location=lambda storage, loc: storage)
if args.env_name is None:
env = DuckietownEnv(
map_name = args.map_name,
domain_rand = False,
draw_bbox = False
)
else:
env = gym.make(args.env_name)
obs = env.reset()
env.render()
total_reward = 0
env.max_steps = math.inf
pi = math.pi
p = [[6.25, 1.75], [6.25, 4.25], [5.35, 4.25], [5.25, 5.25], [1.75, 5.25], [1.75, 1.75]]
tol = 0.1
io = 0
env.cur_pos = [1.5, 0, 1.9]
env.cur_angle = pi
def global_angle_arr(point, i):
t = env.cur_angle
x = env.cur_pos[0]
y = env.cur_pos[2]
