'scene_name': 'bedroom_05',
'terminal_state_id': int(task_scope),
#'initial_state': EVAL_INIT_LOC,
})
real_target_xz.append([xz_numpy[int(task_scope)][0], xz_numpy[int(task_scope)][1]])
ep_rewards = []
ep_lengths = []
ep_collisions = []
scopes = [network_scope, scene_scope, task_scope]
#time.sleep(5)
if 1:
time.sleep(1)
cv2.imshow('target image', env.observation_target)
cv2.waitKey(0)
viewer = SimpleImageViewer()
viewer.imshow(env.observation,str(0))
time.sleep(5)
for i_episode in range(NUM_EVAL_EPISODES):
env.reset()
current_idindex=env.current_state_id
terminal = False
ep_reward = 0
ep_collision = 0
ep_t = 0
ep_action = []
show_target = []
max_value = []
max_index = 0
random_start=False,
random_terminal=False,
whe_show=False,
terminal_id=20,
start_id=0,
whe_use_image=True,
whe_flatten=False,
num_of_frames=1)
# manually disable terminal states
# env.terminals = np.zeros_like(env.terminals)
# env.terminal_states, = np.where(env.terminals)
# env.reset()
# 命令参数初始化
human_agent_action = None
human_wants_restart = False
stop_requested = False
viewer = SimpleImageViewer()
viewer.imshow(env.observation)
viewer.window.on_key_press = key_press
print("Use arrow keys to move the agent.")
print("Press R to reset agent\'s location.")
print("Press Q to quit.")
rollout(env)
print("Goodbye.")
#fo.write('%s \n', %task_scope)
env = Environment({
'scene_name': scene_scope,
'terminal_state_id': task_scope[0],
'checkpoint_state_id': task_scope[1]
})
ep_rewards = []
ep_lengths = []
ep_collisions = []
scopes = [network_scope, scene_scope, task_scope]
print('evaluation: %s %s' % (scene_scope, task_scope))
viewer = SimpleImageViewer()
#NUM_EVAL_EPISODES
for i_episode in range(2):
env.reset()
terminal = False
ep_reward = 0
ep_collision = 0
ep_t = 0
f.write(str(counter * 5 + i_episode) + ': [')
path_x = []
path_y = []
path_x.append(int(env.x * 2))
path_y.append(int(env.z * 2))
int(env.z * 2)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("-s",
"--scene_dump",
type=str,
default="./data/nnew1.h5",
help="path to a hdf5 scene dump file")
parser.add_argument("-n",
"--n_episode",
type=int,
default=20,
help="number of episode to run")
args = parser.parse_args()
print("Loading scene dump {}".format(args.scene_dump))
env = THORDiscreteEnvironment({'h5_file_path': args.scene_dump})
viewer = SimpleImageViewer()
results = []
for i in range(args.n_episode):
env.reset()
graph = build_graph()
results.append(navigate(env, graph))
print("Success for %s times out of %s episode" %
(len(np.array(results).nonzero()), args.n_episode))
viewer.close()
print("Loading scene dump {}".format(args.scene_dump))
env = THORDiscreteEnvironment({
'h5_file_path': args.scene_dump,
'initial_state': args.start,
})
# manually disable terminal states
env.terminals = np.zeros_like(env.terminals)
env.terminal_states, = np.where(env.terminals)
env.reset()
human_agent_action = None
human_wants_restart = False
stop_requested = False
action_list = [int(i)
for i in args.action.split(',')] if args.action else None
action_idx = 0
save_img = None
viewer = SimpleImageViewer(save_dir=args.save_dir)
viewer.imshow(env.observation, save_img=0)
viewer.window.on_key_press = key_press
print("Use arrow keys to move the agent.")
print("Press R to reset agent\'s location.")
print("Press Q to quit.")
rollout(env)
print("Goodbye.")
print("Loading scene dump {}".format(args.scene_dump))
env = THORDiscreteEnvironment({
'h5_file_path': args.scene_dump,
'initial_state': args.start,
})
# manually disable terminal states
env.terminals = np.zeros_like(env.terminals)
env.terminal_states, = np.where(env.terminals)
env.reset()
human_agent_action = None
human_wants_restart = False
stop_requested = False
action_list = [int(i) for i in args.action.split(',')] if args.action else None
action_idx = 0
save_img = None
viewer = SimpleImageViewer(save_dir=args.save_dir)
viewer.imshow(env.observation, save_img=0)
viewer.window.on_key_press = key_press
print("Use arrow keys to move the agent.")
print("Press R to reset agent\'s location.")
print("Press Q to quit.")
rollout(env)
print("Goodbye.")
'terminal_state_id': int(task_scope),
#'initial_state': EVAL_INIT_LOC,
})
real_target_xz.append(
[xz_numpy[int(task_scope)][0], xz_numpy[int(task_scope)][1]])
ep_rewards = []
ep_lengths = []
ep_collisions = []
scopes = [network_scope, scene_scope, task_scope]
#time.sleep(5)
if 1:
time.sleep(1)
cv2.imshow('target image', env.observation_target)
cv2.waitKey(0)
viewer = SimpleImageViewer()
viewer.imshow(env.observation, str(0))
time.sleep(5)
for i_episode in range(NUM_EVAL_EPISODES):
env.reset()
current_idindex = env.current_state_id
terminal = False
ep_reward = 0
ep_collision = 0
ep_t = 0
ep_action = []
show_target = []
max_value = []
max_index = 0
