def load_running_means(self):
# load the observation running mean
if self.args.ckpt_name is not None:
base_path = os.path.join(init_path.get_base_dir(), 'checkpoint')
logger.info('[LOAD_CKPT] loading observation normalizer info')
self.running_mean_info = model_saver.load_numpy_model(
os.path.join(base_path,
self.args.ckpt_name + '_normalizer.npy'),
numpy_var_list=self.running_mean_info)
self.running_mean_info['transfer_env'] = self.args.transfer_env
if not self.args.transfer_env == 'Nothing2Nothing':
if self.args.mlp_raw_transfer == 0:
assert 'shared' in self.args.gnn_embedding_option
ienv, oenv = [
env + '-v1' for env in self.args.transfer_env.split('2')
]
self.running_mean_info = \
structure_mapper.map_transfer_env_running_mean(
ienv, oenv, self.running_mean_info,
self.observation_size,
self.args.gnn_node_option, self.args.root_connection_option,
self.args.gnn_output_option, self.args.gnn_embedding_option
)
def __init__(self,
args,
observation_size,
action_size,
task_q,
result_q,
name_scope='trpo_agent'):
# the multiprocessing initialization
multiprocessing.Process.__init__(self)
self.task_q = task_q
self.result_q = result_q
# the configurations for the agent
self.args = args
# the network parameters
self.name_scope = name_scope
self.observation_size = observation_size
self.action_size = action_size
# the variables and networks to be used, init them before use them
self.policy_network = None
self.policy_var_list = None
self.tf_var_list = None
self.iteration = None
# the gnn parameters
if self.args.use_gnn_as_policy:
self.gnn_parameter_initialization()
self.base_path = init_path.get_base_dir()
def __init__(self):
# get the path of the environments
xml_name = 'WalkersKangaroo.xml'
xml_name = modify_xml(xml_name)
xml_path = os.path.join(
os.path.join(init_path.get_base_dir(), 'environments', 'assets',
xml_name))
xml_path = str(os.path.abspath(xml_path))
mujoco_env.MujocoEnv.__init__(self, xml_path, 4)
utils.EzPickle.__init__(self)
def __init__(self, num=None):
xml_name = 'WalkersFullcheetah.xml'
xml_name = modify_xml(xml_name, num)
xml_path = os.path.join(
os.path.join(init_path.get_base_dir(), 'environments', 'assets',
xml_name))
xml_path = str(os.path.abspath(xml_path))
self.num = num
mujoco_env.MujocoEnv.__init__(self, xml_path, 4)
utils.EzPickle.__init__(self)
def get_output_path(self):
if self.args.output_dir is None:
path = init_path.get_base_dir()
path = os.path.abspath(path)
else:
path = os.path.abspath(self.args.output_dir)
base_path = os.path.join(path, 'csv_log')
if not os.path.exists(base_path):
os.makedirs(base_path)
log_name = os.path.join(
base_path, self.args.task + '_' + self.args.time_id + '.csv')
return log_name
def get_output_path(self, save=True):
if save:
if self.args.output_dir is None:
path = init_path.get_base_dir()
path = os.path.abspath(path)
else:
path = os.path.abspath(self.args.output_dir)
base_path = os.path.join(path, 'checkpoint')
if not os.path.exists(base_path):
os.makedirs(base_path)
model_name = os.path.join(base_path, self.get_experiment_name())
else:
path = self.args.ckpt_name
model_name = path
return model_name
def wrap_env_monitor(self):
if self.allow_monitor:
def video_callback(episode):
return episode % self.args.video_freq < 6
if self.args.output_dir is None:
base_path = init_path.get_base_dir()
else:
base_path = self.args.output_dir
path = os.path.join(base_path, 'video',
self.args.task + '_' + self.args.time_id)
path = os.path.abspath(path)
if not os.path.exists(path):
os.makedirs(path)
self.env = gym.wrappers.Monitor(self.env,
path,
video_callable=video_callback)
def __init__(self, pod_number=2):
# get the path of the environments
xml_name = 'Reacher' + self.get_env_num_str(pod_number) + '.xml'
xml_path = os.path.join(
os.path.join(init_path.get_base_dir(), 'environments', 'assets',
xml_name))
xml_path = str(os.path.abspath(xml_path))
# the environment coeff
self.num_body = pod_number + 1
self._task_indicator = -1.0
self._ctrl_coeff = 2.0 / (self.num_body / 2 + 1)
# norm the max penalty to be 1, max norm is self.num_body * 0.1 * 2
self._dist_coeff = 2.0 / self.num_body
mujoco_env.MujocoEnv.__init__(self, xml_path, 2)
utils.EzPickle.__init__(self)
def __init__(self, sess, summary_name, enable=True, summary_dir=None):
# the interface we need
self.summary = None
self.sess = sess
self.enable = enable
if not self.enable: # the summary handler is disabled
return
if summary_dir is None:
self.path = os.path.join(init_path.get_base_dir(), 'summary')
else:
self.path = os.path.join(summary_dir, 'summary')
self.path = os.path.abspath(self.path)
if not os.path.exists(self.path):
os.makedirs(self.path)
self.path = os.path.join(self.path, summary_name)
self.train_writer = tf.summary.FileWriter(self.path, self.sess.graph)
logger.info('summary write initialized, writing to {}'.format(
self.path))
def __init__(self, CentipedeLegNum=4, is_crippled=False):
# get the path of the environments
if is_crippled:
xml_name = 'CpCentipede' + self.get_env_num_str(CentipedeLegNum) + \
'.xml'
else:
xml_name = 'Centipede' + self.get_env_num_str(CentipedeLegNum) + \
'.xml'
xml_path = os.path.join(init_path.get_base_dir(), 'environments',
'assets', xml_name)
xml_path = str(os.path.abspath(xml_path))
self.num_body = int(np.ceil(CentipedeLegNum / 2.0))
self._control_cost_coeff = .5 * 4 / CentipedeLegNum
self._contact_cost_coeff = 0.5 * 1e-3 * 4 / CentipedeLegNum
self.torso_geom_id = 1 + np.array(range(self.num_body)) * 5
# make sure the centipede is not born to be end of episode
self.body_qpos_id = 6 + 6 + np.array(range(self.num_body)) * 6
self.body_qpos_id[-1] = 5
mujoco_env.MujocoEnv.__init__(self, xml_path, 5)
utils.EzPickle.__init__(self)
# 3. add support for reacher, pendulumm
# 4. MAJOR UPDATE Aug. 21, 2017: now the root only absorb the joints that
# are not motors.
# 5. MAJOR UPDATE, removing all the geom node, Sept. 10th, 2017
# -----------------------------------------------------------------------------
import os
import numpy as np
from bs4 import BeautifulSoup as bs
from tool import init_path
from util import logger
from environments import register
__all__ = ['parse_mujoco_graph']
XML_ASSERT_DIR = os.path.join(init_path.get_base_dir(), 'environments',
'assets')
'''
Definition of nodes:
@root:
The 'root' type is the combination of the top level 'body' node and
the top level free 'joint' (two nodes combined)
Also, additional input will be assigned to the root node
(e.g. the postion of the targer).
For different tasks, we should have different MLP for each root.
@geom, @body, @joint:
The structure defined in the xml files. Ideally, the MLP for input,
propogation, and output could be shared among different models.
'''
#!/usr/bin/env python2
# -----------------------------------------------------------------------------
# @author:
# Tingwu Wang, Jun 23rd, 2017
# -----------------------------------------------------------------------------
import tool.init_path as init_path
from util import logger
import graph_util.mujoco_parser as mujoco_parser
import numpy as np
_BASE_DIR = init_path.get_base_dir()
def map_output(transfer_env, i_value, added_constant, gnn_option_list):
'''
@brief:
i_value could be the logstd (1, num_action), policy_output/w
(64, num_action), policy_output/b (1, num_action)
'''
assert len(gnn_option_list) == 4
i_value = np.transpose(i_value) # make the num_action to the front
ienv, oenv = [env + '-v1' for env in transfer_env.split('2')]
ienv_info = mujoco_parser.parse_mujoco_graph(
ienv,
gnn_node_option=gnn_option_list[0],
root_connection_option=gnn_option_list[1],
gnn_output_option=gnn_option_list[2],
gnn_embedding_option=gnn_option_list[3])
oenv_info = mujoco_parser.parse_mujoco_graph(
oenv,
# are not motors.
# 5. MAJOR UPDATE, removing all the geom node, Sept. 10th, 2017
# -----------------------------------------------------------------------------
import tool.init_path as init_path
import os
import numpy as np
from bs4 import BeautifulSoup as bs
from util import logger
from environments import register
__all__ = ['parse_mujoco_graph']
XML_ASSERT_DIR = os.path.join(init_path.get_base_dir(),
'environments',
'assets')
'''
Definition of nodes:
@root:
The 'root' type is the combination of the top level 'body' node and
the top level free 'joint' (two nodes combined)
Also, additional input will be assigned to the root node
(e.g. the postion of the targer).
For different tasks, we should have different MLP for each root.
@geom, @body, @joint:
The structure defined in the xml files. Ideally, the MLP for input,