args.NU = 30
args.target_v = 0.5
args.boundary = [-0.6, 0.6, 0.3, 0.9, -0.6, 0.6]
# mode 1 corresponds to fixed target
args.MODE = 1
env = Environment(
final_time=args.final_time,
num_steps_per_update=args.num_steps_per_update,
number_of_control_points=args.number_of_control_points,
alpha=args.alpha,
beta=args.beta,
COLLECT_DATA_FOR_POSTPROCESSING=not args.TRAIN,
mode=args.MODE,
target_position=args.target_position,
target_v=args.target_v,
boundary=args.boundary,
E=args.E,
sim_dt=args.sim_dt,
n_elem=args.n_elem,
NU=args.NU,
num_obstacles=8,
COLLECT_CONTROL_POINTS_DATA=not args.TRAIN,
)
name = str(args.algo_name) + "_Case3_id"
identifer = (name + "-" + str(args.timesteps_per_batch) + "-" +
str(args.LAM) + "_" + str(args.SEED) + "_" +
str(args.act_fun_str) + "_" +
get_valid_filename(str(args.net_arch)))
max_rate_of_change_of_activation = np.infty
print("rate of change", max_rate_of_change_of_activation)
# If True, train. Otherwise run trained policy
args.TRAIN = True
env = Environment(
final_time=final_time,
num_steps_per_update=num_steps_per_update,
number_of_control_points=number_of_control_points,
alpha=args.alpha,
beta=args.beta,
COLLECT_DATA_FOR_POSTPROCESSING=not args.TRAIN,
mode=args.mode,
target_position=target_position,
target_v=0.5,
boundary=[-0.6, 0.6, 0.3, 0.9, -0.6, 0.6],
E=1e7,
sim_dt=sim_dt,
n_elem=20,
NU=30,
num_obstacles=0,
dim=3.0,
max_rate_of_change_of_activation=max_rate_of_change_of_activation,
)
name = str(args.algo_name) + "_3d-tracking_id"
identifer = name + "-" + str(args.timesteps_per_batch) + "_" + str(args.SEED)
if args.TRAIN:
log_dir = "./log_" + identifer + "/"
args.beta = 75
args.E = 1e7
args.NU = 30
args.target_v = 0.5
args.boundary = [-0.6, 0.6, 0.3, 0.9, -0.6, 0.6]
env = Environment(
final_time=args.final_time,
num_steps_per_update=args.num_steps_per_update,
number_of_control_points=args.number_of_control_points,
alpha=args.alpha,
beta=args.beta,
COLLECT_DATA_FOR_POSTPROCESSING=not args.TRAIN,
mode=args.MODE,
target_position=args.target_position,
target_v=args.target_v,
boundary=args.boundary,
E=args.E,
sim_dt=args.sim_dt,
n_elem=args.n_elem,
NU=args.NU,
num_obstacles=12,
GENERATE_NEW_OBSTACLES=True,
)
name = str(args.algo_name) + "_nested_regular_id-"
identifer = (name + str(args.mode) + "-" + str(args.number_of_control_points) +
"_" + str(args.alpha) + "_" + str(args.NU) + "_" +
str(args.total_timesteps) + "_" + str(args.timesteps_per_batch) +
"_" + str(args.final_time) + "_" + str(args.SEED))