def visualize_pose_from_expert_data(data_file, camera_id):
expert_traj, pos_data, env_name, dt = load_pose_data(data_file, camera_id)
pos_connection = POS_CONNECTION[env_name]
# import pdb; pdb.set_trace()
image_size = expert_traj[camera_id]['camera_info'][camera_id]['image_size']
image = np.zeros([image_size, image_size, 3], dtype=np.uint8)
env, _ = make_env(env_name, 1234, {})
fig = plt.figure()
for i_pos_id in range(100):
i_pos_data = pos_data[i_pos_id]
# render the image
image = env.render(camera_id=camera_id,
qpos=expert_traj[0]['qpos'][i_pos_id])
fig = plt.figure()
visualize_pose(image, i_pos_data, pos_connection, show=False)
fig.canvas.draw()
plt_results = np.array(fig.canvas.renderer._renderer)
print('Processing %d out of %d' % (i_pos_id, 100))
if i_pos_id == 0:
width, height, _ = plt_results.shape
output_dir = \
data_file.replace('.npy', '_' + str(camera_id) + '.mp4')
video = cv2.VideoWriter(
os.path.join(init_path.get_abs_base_dir(), output_dir),
cv2.VideoWriter_fourcc(*'mp4v'), 40, (height, width))
plt.imshow(plt_results)
video.write(plt_results[:, :, [2, 1, 0]])
plt.close()
video.release()
def __init__(self, env_name):
self._env_name = env_name
self._env, self._env_info = env_register.make_env(self._env_name, 1234)
self._joint_range = \
np.array(self._env._controller_info['range']) / 180.0 * np.pi
self._len_qpos = self._env.get_qpos_size()
self._control_info = self._env.get_controller_info()
def __init__(self, env_name, *args, **kwargs):
remove_render = re.compile(r'__render$')
self.env_name = remove_render.sub('', env_name)
from mbbl.env import env_register
self.env, _ = env_register.make_env(self.env_name, *args, **kwargs)
self.episode_number = 0
# Getting path from logger
self.path = logger._get_path()
self.obs_buffer = []
def main(args=None):
"""Run script.
Args:
args: A list of argument strings to use instead of sys.argv.
"""
args = parse_args(args)
try:
env = gym.make(args.env)
except gym.error.Error:
env, _ = env_register.make_env(args.env,
None,
misc_info={"reset_type": "gym"})
stats = utils.stats.OnlineMeanVariance()
try:
print("Observation Space Shape:", env.observation_space.shape)
except AttributeError:
pass
try:
print("Action Space Shape:", env.action_space.shape)
except AttributeError:
pass
for _ in tqdm.trange(args.num_samples):
state = env.reset()
stats.add(np.asarray(state))
print("State Num Dimensions", len(state))
print("mean\n", stats.mean())
print("var\n", stats.variance())
print("stddev\n", np.sqrt(stats.variance()))
try:
print()
print(env.metadata["initial_state.mean"])
print(np.diag(env.metadata["initial_state.covariance"]))
print(np.sqrt(np.diag(env.metadata["initial_state.covariance"])))
except (AttributeError, KeyError):
pass
def _build_env(self):
self._env, self._env_info = env_register.make_env(
self.args.task, self._npr.randint(0, 9999),
{'allow_monitor': self.args.monitor and self._worker_id == 0})
self._env_solved_reward = self._env_info['SOLVED_REWARD'] \
if 'SOLVED_REWARD' in self._env_info else np.inf
def make_norm_env(cfg):
if 'gym' in cfg.env_name:
from mbbl.env.env_register import make_env
misc_info = {'reset_type': 'gym'}
if 'gym_pets' in cfg.env_name:
misc_info['pets'] = True
env, meta = make_env(cfg.env_name, rand_seed=cfg.seed, misc_info=misc_info)
env.metadata = env._env.metadata
env.reward_range = env._env.reward_range
env.spec = env._env.spec
env.unwrapped = env._env.unwrapped
# env._configured = env._env._configured
env.close = env._env.close
env = RescaleAction(env, -1., 1.)
# assert np.all(env._env.action_space.high == env._env.action_space.high)
assert not cfg.max_episode_steps
# env.action_space = env._env.action_space
if cfg.env_name == 'gym_fswimmer' or 'gym_pets' in cfg.env_name:
env._max_episode_steps = env.env._env_info['max_length']
else:
env._max_episode_steps = env.env._env._max_episode_steps
def render(mode, height, width, camera_id):
frame = env.env._env.render(mode='rgb_array')
return frame
env.render = render
def set_seed(seed):
if 'gym_pets' in cfg.env_name or cfg.env_name == 'gym_fswimmer':
return env.env._env.seed(seed)
else:
return env.env._env.env.seed(seed)
elif cfg.env_name == 'Humanoid-v2':
env = gym.make('Humanoid-v2')
env = RescaleAction(env, -1., 1.)
assert not cfg.max_episode_steps
env._max_episode_steps = env.env._max_episode_steps
def render(mode, height, width, camera_id):
frame = env.env.render(mode='rgb_array')
return frame
env.render = render
def set_seed(seed):
return env.env.seed(seed)
elif cfg.env_name == 'pets_cheetah':
from svg.env import register_pets_environments
register_pets_environments()
env = gym.make('PetsCheetah-v0')
env = RescaleAction(env, -1., 1.)
assert not cfg.max_episode_steps
env._max_episode_steps = env.env._max_episode_steps
def render(mode, height, width, camera_id):
frame = env.env.render(mode='rgb_array')
return frame
env.render = render
def set_seed(seed):
return env.env.seed(seed)
elif cfg.env_name == 'pets_reacher':
from svg.env import register_pets_environments
register_pets_environments()
env = gym.make('PetsReacher-v0')
env = RescaleAction(env, -1., 1.)
assert not cfg.max_episode_steps
env._max_episode_steps = env.env._max_episode_steps
def render(mode, height, width, camera_id):
frame = env.env.render(mode='rgb_array')
return frame
env.render = render
def set_seed(seed):
return env.env.seed(seed)
elif cfg.env_name == 'pets_pusher':
from svg.env import register_pets_environments
register_pets_environments()
env = gym.make('PetsPusher-v0')
env = RescaleAction(env, -1., 1.)
assert not cfg.max_episode_steps
env._max_episode_steps = env.env._max_episode_steps
def render(mode, height, width, camera_id):
frame = env.env.render(mode='rgb_array')
return frame
env.render = render
def set_seed(seed):
return env.env.seed(seed)
elif cfg.env_name == 'mbpo_hopper':
env = gym.make('Hopper-v2')
env = RescaleAction(env, -1., 1.)
assert not cfg.max_episode_steps
env._max_episode_steps = env.env._max_episode_steps
def render(mode, height, width, camera_id):
frame = env.env.render(mode='rgb_array')
return frame
env.render = render
def set_seed(seed):
return env.env.seed(seed)
elif cfg.env_name == 'mbpo_walker2d':
env = gym.make('Walker2d-v2')
env = RescaleAction(env, -1., 1.)
assert not cfg.max_episode_steps
env._max_episode_steps = env.env._max_episode_steps
# env.reset_old = env.reset
# env.reset = lambda: env.reset_old()[0]
def render(mode, height, width, camera_id):
frame = env.env.render(mode='rgb_array')
return frame
env.render = render
def set_seed(seed):
return env.env.seed(seed)
elif cfg.env_name == 'mbpo_ant':
from .env import register_mbpo_environments
register_mbpo_environments()
env = gym.make('AntTruncatedObs-v2')
env = RescaleAction(env, -1., 1.)
assert not cfg.max_episode_steps
env._max_episode_steps = env.env._max_episode_steps
def render(mode, height, width, camera_id):
frame = env.env.render(mode='rgb_array')
return frame
env.render = render
def set_seed(seed):
return env.env.seed(seed)
elif cfg.env_name == 'mbpo_cheetah':
from svg.env import register_mbpo_environments
register_mbpo_environments()
env = gym.make('HalfCheetah-v2')
env = RescaleAction(env, -1., 1.)
assert not cfg.max_episode_steps
env._max_episode_steps = env.env._max_episode_steps
def render(mode, height, width, camera_id):
frame = env.env.render(mode='rgb_array')
return frame
env.render = render
def set_seed(seed):
return env.env.seed(seed)
elif cfg.env_name == 'mbpo_humanoid':
from svg.env import register_mbpo_environments
register_mbpo_environments()
env = gym.make('HumanoidTruncatedObs-v2')
env = RescaleAction(env, -1., 1.)
assert not cfg.max_episode_steps
env._max_episode_steps = env.env._max_episode_steps
def render(mode, height, width, camera_id):
frame = env.env.render(mode='rgb_array')
return frame
env.render = render
def set_seed(seed):
return env.env.seed(seed)
else:
assert cfg.env_name.startswith('dmc_')
env = dmc.make(cfg)
if cfg.pixels:
env = FrameStack(env, k=cfg.frame_stack)
def set_seed(seed):
return env.env.env._env.task.random.seed(seed)
else:
def set_seed(seed):
return env.env._env.task.random.seed(seed)
env.set_seed = set_seed
return env
TEST = "DYNAMICS_DERIVATIVE" # 'REWARD_DERIVATIVE'
# TEST = 'REWARD_DERIVATIVE'
# candidate_names = invertedPendulum.env.PENDULUM
# candidate_names = pendulum.env.PENDULUM
# candidate_names = pets.env.ENV
candidate_names = humanoid.env.ENV
if __name__ == '__main__' and TEST == 'REWARD_DERIVATIVE':
DERIVATIVE_EPS = 1e-6
# test the walker
# candidate_names = walker.env.WALKER
# candidate_names = reacher.env.ARM_2D
max_error = 0.0
for env_name in candidate_names:
env, _ = env_register.make_env(env_name, 123, {})
env_info = env_register.get_env_info(env_name)
derivative_env, _ = env_register.make_env(env_name, 234, {})
data_dict = \
{'action': np.random.uniform(-1, 1, [1, env_info['action_size']])}
data_dict['start_state'], _, _, _ = env.reset()
data_dict['start_state'] = data_dict['start_state'].reshape(1, -1)
data_dict['start_state'] = data_dict['start_state'].reshape(1, -1)
r_u = derivative_env.reward_derivative(data_dict, 'action')
r_uu = derivative_env.reward_derivative(data_dict, 'action-action')
r_x = derivative_env.reward_derivative(data_dict, 'state')
r_xx = derivative_env.reward_derivative(data_dict, 'state-state')
# test the derivative of the reward wrt action
def build_network(self):
self._env, self._env_info = env_register.make_env(
self.args.task_name, self._npr.randint(0, 9999))
def _build_env(self):
self._env, self._env_info = env_register.make_env(
self.args.task, self._npr.randint(0, 999999),
{'allow_monitor': self.args.monitor})
# import matplotlib.pyplot as plt
# from mbbl.env.dm_env.pos_dm_env import POS_CONNECTION
from mbbl.env.env_register import make_env
# from mbbl.util.common import logger
# from mbbl.config import init_path
# import cv2
# import os
# from skimage import draw
import numpy as np
# from mbbl.util.il.expert_data_util import load_pose_data
# import argparse
if __name__ == '__main__':
env, env_info = make_env("cheetah-run-pos", 1234)
control_info = env.get_controller_info()
dynamics_env, _ = make_env("cheetah-run-pos", 1234)
# generate the data
env.reset()
for i in range(1000):
action = np.random.randn(env_info['action_size'])
qpos = np.array(env._env.physics.data.qpos, copy=True)
old_qpos = np.array(env._env.physics.data.qpos, copy=True)
old_qvel = np.array(env._env.physics.data.qvel, copy=True)
old_qacc = np.array(env._env.physics.data.qacc, copy=True)
old_qfrc_inverse = np.array(env._env.physics.data.qfrc_inverse, copy=True)
_, _, _, _ = env.step(action)
ctrl = np.array(env._env.physics.data.ctrl, copy=True)
qvel = np.array(env._env.physics.data.qvel, copy=True)
def _build_env(self):
self._env, self._env_info = env_register.make_env(
self.args.task, self._npr.randint(0, 9999),
{'allow_monitor': self.args.monitor and self._worker_id == 0})
self._fake_env = fake_env(self._env, self._step)
def build_network(self):
# the placeholders
self._env, self._env_info = env_register.make_env(
self.args.task_name, self._npr.randint(0, 9999))
self._env.reset()
assert hasattr(self._env, 'fdynamics')
def __init__(self, env_name):
self.env, _ = make_env(env_name, 1234)
self.env.reset()
def get_env_info(env_name, env_seed, dtype):
""" @brief: In this function return the reward moment function, initial
state mean and initial state covariance.
"""
env, env_info = env_register.make_env(
env_name, env_seed, misc_info={"reset_type": "gym"}
)
# the reward moment function
if env_name == "gym_walker2d":
reward_moment_map = reward_functions.mbbl_walker_reward(
8, # velocity index
0, # height index
1.3, # target_height
3.0, # height_coefficient
slice(17, 17 + 6), # actions
0.1, # action coeff
)
elif env_name == "gym_hopper":
reward_moment_map = reward_functions.mbbl_walker_reward(
5, # velocity index
0, # height index
1.3, # target_height
3.0, # height_coefficient
slice(11, 11 + 3), # actions
0.1, # action coeff
)
elif env_name == "gym_swimmer":
reward_moment_map = reward_functions.mbbl_walker_reward(
3, # velocity index
0, # height index
0.0, # target_height
0.0, # height_coefficient
slice(8, 10), # actions
0.0001, # action coeff
)
elif env_name == "gym_fswimmer":
reward_moment_map = reward_functions.fswimmer_walker_reward(
8, # velocity index
0, # height index
0.0, # target_height
0.0, # height_coefficient
slice(8, 10), # actions
0.0001, # action coeff
)
elif "gym_cheetah" in env_name:
reward_moment_map = reward_functions.mbbl_walker_reward(
8, # velocity index
0, # height index
0.0, # target_height
0.0, # height_coefficient
slice(17, 17 + 6), # actions
0.1, # action coeff
)
elif env_name == "gym_ant":
reward_moment_map = reward_functions.mbbl_walker_reward(
13, # velocity index
0, # height index
0.57, # target_height
3.0, # height_coefficient
slice(27, 27 + 8), # actions
0.1, # action coeff
)
elif env_name == "gym_reacher":
# Action space has size 2. Distance index is [-3:len(state)]
# So [-5:] gets both
reward_moment_map = reward_functions.surrogate_reacher_reward(
distance_index=slice(-5, None)
)
elif env_name == "gym_acrobot":
reward_moment_map = reward_functions.acrobot_reward()
elif "gym_cartpole" in env_name:
reward_moment_map = reward_functions.cartpole_reward(
position_index=0, position_coefficient=0.01, angle_index=2
)
elif env_name == "gym_mountain":
reward_moment_map = reward_functions.mountain_car_reward()
elif "gym_pendulum" in env_name:
reward_moment_map = reward_functions.pendulum_reward(slice(3, 4))
elif env_name == "gym_invertedPendulum":
reward_moment_map = reward_functions.inverted_pendulum_reward()
else:
raise NotImplementedError
# the mean of the initial state
initial_state_mean = np.array(np.reshape(get_x0(env_name), [-1]), dtype=np.float64)
# the cov of the initial state
initial_state_covariance = get_covX0(env_name, env_info)
return (
env,
env_info,
np.array(initial_state_mean, dtype=dtype),
np.array(initial_state_covariance, dtype=dtype),
reward_moment_map,
)
parser.add_argument("--env_name",
type=str,
required=False,
default='cheetah-run-pos')
parser.add_argument("-t",
"--visualize_type",
type=str,
required=False,
help="The directory of the expert data file",
default="inverse_action")
parser.add_argument("--sol_qpos_freq", type=int, required=False, default=1)
parser.add_argument("--traj_id", type=int, required=False, default=0)
parser.add_argument("--traj_length",
type=int,
required=False,
default=1000)
args = parser.parse_args()
env, env_info = make_env(args.env_name, 1234)
control_info = env.get_controller_info()
expert_data = load_pose_data(args.expert_data_name, args.camera_id)
# how good is the intepolation methods?
plot_interpolation(expert_data[args.traj_id], args.sol_qpos_freq,
args.traj_length)
# how accurate is inverse dynamics?
plot_inverse_dynamics_stats(env, expert_data[args.traj_id],
args.sol_qpos_freq, control_info,
args.traj_length)