def random_sample():
# mean trajectory length over 100 trials for a newly initialized policy
# random_sample_policy
# pos1 6.28
# pos2 6.47
# pos3 6.30
# pos_test 6.94
# resetHandEasy 5.78
parameters = OriginalParams(Parmap(time_step=1e-2, nb_iter=500))
env = AllegroHand(parameters)
mdim_vals = {}
name_pathl = {}
# try:
tf.reset_default_graph()
sess = tf.Session()
num_tra = 100
with tf.compat.v1.variable_scope("random_sample_policy"):
policy = MLPGaussianPolicy(sess, env.action_space.shape[0], env.observation_space.shape[0],
mean_mult=parameters.mean_mult,
init_sigma=parameters.init_sigma,
min_sigma=parameters.min_sigma)
sess.run(tf.compat.v1.global_variables_initializer())
all_positions = position_list.copy()
all_positions.append(pos_test)
for position_func in all_positions:
nb_traj_length = run_lower_with_environment(position_func, env, lambda obs: policy.get_action(obs), num_trajectories=num_tra)
# nb_traj_length = run_lower_policy(position_func, params, policy, num_trajectories=num_tra, show=True)
name_pathl.update({position_func.__name__: nb_traj_length})
env.close()
U.get_session().close()
# except Exception as Id:
# print(name, 'not found in meta')
mdim_vals.update({"random_sample_policy": name_pathl})
return pd.DataFrame(mdim_vals)
def save_position_as_images(res=1600):
env = AllegroHand(Parmap(), gui=True, show_fingers=True)
for pos in all_pos_list:
# env.pc.reset_debug_visualizer_camera(CamProps.camera_distance,
# CamProps.camera_yaw,
# CamProps.camera_pitch,
# CamProps.camera_target_position)
env = pos(env)
env = adhere_target_to_initial(env)
env.pc.time_step = 1e-3
env.pc.step_simulation()
camera_image = env.pc.call(pybullet.getCameraImage, res, res)
rbg_image = camera_image[2]
img = Image.fromarray(rbg_image[:, :, 0:3]) # discard alpha channel
img.save(''+pos.__name__+'.pdf')
print("break")
time.sleep(1)
env.close()
def multi_wrapper(args):
pars = OriginalParams(Parmap(time_step=1e-2, nb_iter=500, discount=.99))
rwd_function = args[1]
pars.rwd_func = rwd_function
t = time.asctime().replace(" ", "_").replace(":", "_")
run_name_suffix = rwd_function.__name__ + str(t)
pars.run_name = run_name_suffix + pars.run_name
env = AllegroCollisionFromTactileHand(pars, gui=False, show_fingers=False)
tf.reset_default_graph()
sess = tf.Session()
learn_ppo(session=sess, env=env, parameters=pars, dir_out="True")
print("reset")
time.sleep(0.1)
U.get_session().close()
env.close()
time.sleep(0.1)
return args[0]
def multi_wrapper(args):
pars = OriginalParams(
Parmap(time_step=1e-2, nb_iter=600, movement_noise=args))
pars.rwd_func = weighted_or_dist
t = time.asctime().replace(" ", "_").replace(":", "_")
run_name_suffix = "SimulatedNoise_{}_".format(str(t))
pars.run_name = run_name_suffix + pars.run_name
env = AllegronNoiseFromUpper(pars, gui=False, show_fingers=False)
tf.reset_default_graph()
sess = tf.Session()
learn_ppo(session=sess, env=env, parameters=pars, dir_out="True")
print("reset")
time.sleep(0.1)
U.get_session().close()
env.close()
time.sleep(0.1)
return args
def get_non_trained_env():
pars = UpperParmap(Parmap())
env = AllegroHand(pars)
return env, pars
from setting_utils.param_handler import Parmap, TB_DIR, OriginalParams
import time
import baselines.common.tf_util as U
from setting_utils.rewards import move_connection_to_object
import numpy as np
os.environ['OPENAI_LOG_FORMAT'] = 'stdout,log,csv,tensorboard'
os.environ['OPENAI_LOGDIR'] = TB_DIR
import tensorflow as tf
from ausy_base.learn_policy import learn_on_env as learn_ppo
if __name__ == "__main__":
t = time.asctime().replace(" ", "_").replace(":", "_")
print('Start run at %s' % t)
pars = OriginalParams(Parmap(nb_iter=300))
pars.rwd_func = move_connection_to_object
env = AllegroCollisionFromTactileHand(pars, gui=False, show_fingers=False)
# logging
run_name_suffix = "TestingNewSaveDir_{}_".format(str(t))
pars.run_name = run_name_suffix + pars.run_name
print(dict(**vars(pars)))
# init session
tf.reset_default_graph()
sess = tf.Session()
# learn
learn_ppo(session=sess, env=env, parameters=pars, dir_out="True")
# clean session close
time.sleep(0.1)
U.get_session().close()
from pybullet_envs.gym_locomotion_envs import HumanoidBulletEnv
from setting_utils.param_handler import Parmap
from ausy_base.learn_policy import learn_on_env as learn_ppo
import time
import tensorflow as tf
if __name__ == '__main__':
pars = Parmap()
#env = Walker2DBulletEnv(render=True)
env = HumanoidBulletEnv(render=False)
# for logging
t = time.asctime().replace(" ", "_").replace(":", "_")
run_name_suffix = "PPO_test_{}_".format(str(t))
pars.run_name = run_name_suffix + pars.run_name
tf.reset_default_graph()
sess = tf.Session()
pars.nb_iter = 300000
learn_ppo(session=sess, env=env, parameters=pars, dir_out="True")
# CamProps.camera_pitch,
# CamProps.camera_target_position)
env = pos(env)
env = adhere_target_to_initial(env)
env.pc.time_step = 1e-3
env.pc.step_simulation()
camera_image = env.pc.call(pybullet.getCameraImage, res, res)
rbg_image = camera_image[2]
img = Image.fromarray(rbg_image[:, :, 0:3]) # discard alpha channel
img.save(''+pos.__name__+'.pdf')
print("break")
time.sleep(1)
env.close()
if __name__ == '__main__':
pars = Parmap(time_step=1e-2)
pars.rwd_func = lambda x: (0, False)
env = AllegroCollisionFromTactileHand(pars, gui=True, show_fingers=True)
all_pos = all_pos_list.copy()
for pos in all_pos:
env = pos(env)
zero_act = get_joint_state(env.hand)
env.pc.step_simulation()
for i in range(3):
# put debugger here, and use console to prototype new hand-object configurations
env.show_collisions()
tac_state_dict = tactile_state_dict(env.hand)
print(env.reward())
for finger in tac_state_dict:
print(finger, np.sum(tac_state_dict[finger]))