def game_process(conn):
env = HuskyNavigateEnv(config='gibson_configs/beechwood_c0_rgb_skip50_random_initial_separate.yaml')
conn.send(0)
done = False
while True:
cmd, arg = conn.recv()
if cmd=='reset':
ob = env.reset()
done = False
conn.send(ob)
elif cmd=='action':
ob, reward, done, info = env.step(arg)
r, p, yaw = env.robot.body_rpy
rot_speed = np.array(
[[np.cos(-yaw), -np.sin(-yaw), 0],
[np.sin(-yaw), np.cos(-yaw), 0],
[ 0, 0, 1]]
)
vx, vy, vz = np.dot(rot_speed, env.robot.robot_body.speed())
avx, avy, avz = np.dot(rot_speed, env.robot.robot_body.angular_speed())
vel = [vx, vy, vz, avx, avy, avz]
conn.send([ob, reward, running, vel])
elif cmd=='observe':
eye_pos, eye_quat = env.get_eye_pos_orientation()
pose = [eye_pos, eye_quat]
ob = env.render_observations(pose)
conn.send([ob])
elif cmd=='running':
running = not done
conn.send([running])
elif cmd=='stop':
break
env.close()
conn.send(0)
conn.close()
def train(num_timesteps, seed):
rank = MPI.COMM_WORLD.Get_rank()
#sess = U.single_threaded_session()
sess = utils.make_gpu_session(args.num_gpu)
sess.__enter__()
if args.meta != "":
saver = tf.train.import_meta_graph(args.meta)
saver.restore(sess, tf.train.latest_checkpoint('./'))
if rank == 0:
logger.configure()
else:
logger.configure(format_strs=[])
workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank()
set_global_seeds(workerseed)
use_filler = not args.disable_filler
config_file = os.path.join(os.path.dirname(os.path.realpath(__file__)),
'..', 'configs', 'husky_navigate_train.yaml')
print(config_file)
env = HuskyNavigateEnv(is_discrete=True,
gpu_count=args.gpu_count,
config=config_file)
policy = policy_fn("pi", env.observation_space,
env.action_space) # Construct network for new policy
def execute_policy(env):
ob, ob_sensor = env.reset()
stochastic = True
while True:
#with Profiler("agent act"):
ac, vpred = policy.act(stochastic, ob)
ob, rew, new, meta = env.step(ac)
if new:
ob, ob_sensor = env.reset()
env = bench.Monitor(
env,
logger.get_dir() and osp.join(logger.get_dir(), str(rank)))
env.seed(workerseed)
gym.logger.setLevel(logging.WARN)
pposgd_simple.enjoy(env,
policy_fn,
max_timesteps=int(num_timesteps * 1.1),
timesteps_per_actorbatch=1024,
clip_param=0.2,
entcoeff=0.01,
optim_epochs=4,
optim_stepsize=1e-3,
optim_batchsize=64,
gamma=0.99,
lam=0.95,
schedule='linear',
save_per_acts=50,
reload_name=args.reload_name)
env.close()
def train(num_timesteps, seed):
rank = MPI.COMM_WORLD.Get_rank()
#sess = U.single_threaded_session()
sess = utils.make_gpu_session(args.num_gpu)
sess.__enter__()
if args.meta != "":
saver = tf.train.import_meta_graph(args.meta)
saver.restore(sess,tf.train.latest_checkpoint('./'))
if rank == 0:
logger.configure()
else:
logger.configure(format_strs=[])
workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank()
set_global_seeds(workerseed)
use_filler = not args.disable_filler
config_file = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', 'configs',
'husky_navigate_enjoy.yaml')
print(config_file)
env = HuskyNavigateEnv(gpu_idx=args.gpu_idx, config = config_file)
def policy_fn(name, ob_space, ac_space):
if args.mode == "SENSOR":
return mlp_policy.MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space, hid_size=64, num_hid_layers=2)
else:
#return fuse_policy.FusePolicy(name=name, ob_space=ob_space, sensor_space=sensor_space, ac_space=ac_space, save_per_acts=10000, session=sess)
#else:
return cnn_policy.CnnPolicy(name=name, ob_space=ob_space, ac_space=ac_space, save_per_acts=10000, session=sess, kind='small')
env = Monitor(env, logger.get_dir() and
osp.join(logger.get_dir(), str(rank)))
env.seed(workerseed)
gym.logger.setLevel(logging.WARN)
pposgd_simple.enjoy(env, policy_fn,
max_timesteps=int(num_timesteps * 1.1),
timesteps_per_actorbatch=1024,
clip_param=0.2, entcoeff=0.01,
optim_epochs=4, optim_stepsize=1e-3, optim_batchsize=64,
gamma=0.99, lam=0.95,
schedule='linear',
save_per_acts=50,
sensor=args.mode=="SENSOR",
reload_name=args.reload_name
)
env.close()
os.mkdir(RGB_PATH + 'rgb') #create rgb dir
os.mkdir(RGB_PATH + 'depth')
pos = get_present_pos_with_robotheight((x, y, z_position))
orn = (3.14, 0, random.uniform(0, 2 * np.pi))
if not setting_initial_point(pos, orn):
continue
#p.setGravity(0,0,0)#Set gravity in three axis to be 0
obj_id = env.robot.robot_ids
ground_id, = env.ground_ids
enableCollision = 0 #1: ENABLE 0:DISALE
#p.setCollisionFilterPair(obj_id[0],ground_id[0],-1,-1,enableCollision)
for n in range(NUMBER_OF_ACTION):
pos, orn = trajectory_generator(
pos, orn, bimap, map_para, RGB_PATH)
if pos == 0: #abandon the trajectory which trap the robot
shutil.rmtree(PATH + 'data/' + str((x, y)))
break
else:
number_of_trajectory -= 1
#cv2.imwrite(PATH+'tra_bin.png',bimap)
cv2.imwrite(PATH + 'bi_map_adj.png', bimap)
print('model:', folder, 'floor:', floor, 'done!')
LIMITED_MODEL -= 1
env.close()
print('done')
def train(seed):
rank = MPI.COMM_WORLD.Get_rank()
#sess = U.single_threaded_session()
sess = utils.make_gpu_session(args.num_gpu)
sess.__enter__()
if args.meta != "":
saver = tf.train.import_meta_graph(args.meta)
saver.restore(sess, tf.train.latest_checkpoint('./'))
if rank == 0:
logger.configure()
else:
logger.configure(format_strs=[])
workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank()
set_global_seeds(workerseed)
use_filler = not args.disable_filler
config_file = os.path.join(os.path.dirname(os.path.realpath(__file__)),
'..', 'configs', 'config_husky_enjoy.yaml')
#config_file = os.path.join(os.path.dirname(os.path.realpath(__file__)),'..','configs','husky_gibson_flagrun_train.yaml')
print(config_file)
env = HuskyNavigateEnv(gpu_idx=args.gpu_idx, config=config_file)
#env = HuskyGibsonFlagRunEnv(gpu_idx=args.gpu_idx, config = config_file)
step = env.config['n_step']
episode = env.config['n_episode']
iteration = env.config['n_iter']
target = env.config['target_pos']
elm_policy = env.config['elm_active']
num_timesteps = step * episode * iteration
tpa = step * episode
if args.mode == "SENSOR": # Blind Mode
def policy_fn(name, ob_space, ac_space):
return mlp_policy.MlpPolicy(name=name,
ob_space=ob_space,
ac_space=ac_space,
hid_size=128,
num_hid_layers=4,
elm_mode=elm_policy)
elif args.mode == "DEPTH" or args.mode == "RGB": # Fusing sensor space with image space
def policy_fn(name, ob_space, sensor_space, ac_space):
return fuse_policy.FusePolicy(name=name,
ob_space=ob_space,
sensor_space=sensor_space,
ac_space=ac_space,
save_per_acts=10000,
hid_size=128,
num_hid_layers=4,
session=sess,
elm_mode=elm_policy)
elif args.mode == "RESNET":
def policy_fn(name, ob_space, sensor_space, ac_space):
return resnet_policy.ResPolicy(name=name,
ob_space=ob_space,
sensor_space=sensor_space,
ac_space=ac_space,
save_per_acts=10000,
hid_size=128,
num_hid_layers=4,
session=sess,
elm_mode=elm_policy)
elif args.mode == "ODE":
def policy_fn(name, ob_space, sensor_space, ac_space):
return ode_policy.ODEPolicy(name=name,
ob_space=ob_space,
sensor_space=sensor_space,
ac_space=ac_space,
save_per_acts=10000,
hid_size=128,
num_hid_layers=4,
session=sess,
elm_mode=elm_policy)
else: # Using only image space
def policy_fn(name, ob_space, ac_space):
return cnn_policy.CnnPolicy(name=name,
ob_space=ob_space,
ac_space=ac_space,
session=sess,
kind='small')
env = Monitor(env,
logger.get_dir() and osp.join(logger.get_dir(), str(rank)))
env.seed(workerseed)
gym.logger.setLevel(logging.WARN)
args.reload_name = '/home/berk/PycharmProjects/Gibson_Exercise/gibson/utils/models/PPO_ODE_2020-12-05_500_50_137_150.model'
#args.reload_name = '/home/berk/PycharmProjects/Original_Gibs/gibson/utils/models/flagrun_RGBD2_150.model'
print(args.reload_name)
modes_camera = ["DEPTH", "RGB", "RESNET", "ODE"]
if args.mode in modes_camera:
pposgd_fuse.enjoy(env,
policy_fn,
max_timesteps=int(num_timesteps * 1.1),
timesteps_per_actorbatch=tpa,
clip_param=0.2,
entcoeff=0.03,
optim_epochs=4,
optim_stepsize=1e-3,
optim_batchsize=64,
gamma=0.99,
lam=0.95,
schedule='linear',
save_name="PPO_{}_{}_{}_{}_{}".format(
args.mode, datetime.date.today(), step, episode,
iteration),
save_per_acts=10,
reload_name=args.reload_name)
else:
if args.mode == "SENSOR": sensor = True
else: sensor = False
pposgd_simple.enjoy(env,
policy_fn,
max_timesteps=int(num_timesteps * 1.1),
timesteps_per_actorbatch=tpa,
clip_param=0.2,
entcoeff=0.03,
optim_epochs=4,
optim_stepsize=1e-3,
optim_batchsize=64,
gamma=0.996,
lam=0.95,
schedule='linear',
save_name="PPO_{}_{}_{}_{}_{}".format(
args.mode, datetime.date.today(), step,
episode, iteration),
save_per_acts=10,
sensor=sensor,
reload_name=args.reload_name)
env.close()