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]