def __init__(self, config, quit_on_end=False):
"""
Initialize GPSMain
Args:
config: Hyperparameters for experiment
quit_on_end: When true, quit automatically on completion
"""
self._quit_on_end = quit_on_end
self._hyperparams = config
self._conditions = config['common']['conditions']
if 'train_conditions' in config['common']:
self._train_idx = config['common']['train_conditions']
self._test_idx = config['common']['test_conditions']
else:
self._train_idx = range(self._conditions)
config['common']['train_conditions'] = config['common'][
'conditions']
self._hyperparams = config
self._test_idx = self._train_idx
self._data_files_dir = config['common']['data_files_dir']
self.agent = config['agent']['type'](config['agent'])
self.data_logger = DataLogger()
self.gui = GPSTrainingGUI(
config['common']) if config['gui_on'] else None
config['algorithm']['agent'] = self.agent
self.algorithm = config['algorithm']['type'](config['algorithm'])
def __init__(self, config, quit_on_end=False):
"""
Initialize GPSMain
Args:
config: Hyperparameters for experiment
quit_on_end: When true, quit automatically on completion
"""
self._quit_on_end = quit_on_end
self._hyperparams = config
# print(config)
self._conditions = config['common']['conditions']
if 'train_conditions' in config['common']:
self._train_idx = config['common']['train_conditions']
self._test_idx = config['common']['test_conditions']
else:
self._train_idx = range(self._conditions)
config['common']['train_conditions'] = config['common'][
'conditions']
self._hyperparams = config
self._test_idx = self._train_idx
self._data_files_dir = config['common']['data_files_dir']
self.agent = config['agent']['type'](config['agent'])
self.data_logger = DataLogger()
self.gui = GPSTrainingGUI(
config['common']) if config['gui_on'] else None
config['algorithm']['agent'] = self.agent
# hard code to pass the map_state and target_state
config['algorithm']['cost']['costs'][1]['data_types'][3][
'target_state'] = config['agent']['target_state']
config['algorithm']['cost']['costs'][1]['data_types'][3][
'map_size'] = config['agent']['map_size']
# config['algorithm']['cost']['costs'][1]['data_types'][3]['map_size'] = CUT_MAP_SIZE
if len(config['algorithm']['cost']['costs']) > 2:
# temporarily deprecated, not considering collision cost
# including cost_collision
config['algorithm']['cost']['costs'][2]['data_types'][3][
'target_state'] = config['agent']['target_state']
config['algorithm']['cost']['costs'][2]['data_types'][3][
'map_size'] = config['agent']['map_size']
config['algorithm']['cost']['costs'][2]['data_types'][3][
'map_state'] = config['agent']['map_state']
# print(config['algorithm'])
self.algorithm = config['algorithm']['type'](config['algorithm'])
# Modified by RH
self.finishing_time = None
self.U = None
self.final_pos = None
self.samples = []
self.quick_sample = None
# self.map_size = config['agent']['map_size']
self.map_size = CUT_MAP_SIZE
self.display_center = config['agent']['display_center']
def __init__(self, config):
self._hyperparams = config
self._conditions = config['common']['conditions']
self._data_files_dir = config['common']['data_files_dir']
self.agent = config['agent']['type'](config['agent'])
self.data_logger = DataLogger()
self.gui = GPSTrainingGUI(
config['common']) if config['gui_on'] else None
config['algorithm']['agent'] = self.agent
self.algorithm = config['algorithm']['type'](config['algorithm'])
def __init__(self, config):
"""
Initialize GPSMain
Args:
config: Hyperparameters for experiment
quit_on_end: When true, quit automatically on completion
"""
self._quit_on_end = quit_on_end
self._hyperparams = config
self._conditions = config['common']['conditions']
if 'train_conditions' in config['common']:
self._train_idx = config['common']['train_conditions']
self._test_idx = config['common']['test_conditions']
else:
self._train_idx = range(self._conditions)
config['common']['train_conditions'] = config['common'][
'conditions']
self._hyperparams = config
self._test_idx = self._train_idx
self._data_files_dir = config['common']['data_files_dir']
self.agent = config['agent']['type'](config['agent'])
self.data_logger = DataLogger()
self.gui = GPSTrainingGUI(
config['common']) if config['gui_on'] else None
config['algorithm']['agent'] = self.agent
self.algorithm = config['algorithm']['type'](config['algorithm'])
self.algorithm.init_samples = self._hyperparams['num_samples']
if self.algorithm._hyperparams['ioc']:
demo_file = self._data_files_dir + 'demos.pkl'
demos = self.data_logger.unpickle(demo_file)
if demos is None:
self.demo_gen = GenDemo(config)
self.demo_gen.ioc_algo = self.algorithm
self.demo_gen.generate()
demo_file = self._data_files_dir + 'demos.pkl'
demos = self.data_logger.unpickle(demo_file)
config['agent']['pos_body_offset'] = demos['pos_body_offset']
self.agent = config['agent']['type'](config['agent'])
self.algorithm.demoX = demos['demoX']
self.algorithm.demoU = demos['demoU']
self.algorithm.demoO = demos['demoO']
def __init__(self, config, quit_on_end=False, no_algorithm=False):
"""
Initialize GPSMain
Args:
config: Hyperparameters for experiment
quit_on_end: When true, quit automatically on completion
"""
self._quit_on_end = quit_on_end
self._hyperparams = config
self._conditions = config['common']['conditions']
if 'train_conditions' in config['common']:
self._train_idx = config['common']['train_conditions']
self._test_idx = config['common']['test_conditions']
else:
self._train_idx = range(self._conditions)
config['common']['train_conditions'] = config['common'][
'conditions']
self._hyperparams = config
self._test_idx = self._train_idx
self._data_files_dir = config['common']['data_files_dir']
config['agent']['data_files_dir'] = self._data_files_dir
config['algorithm']['data_files_dir'] = self._data_files_dir
self.agent = config['agent']['type'](config['agent'])
self.data_logger = DataLogger()
self.gui = None
if config['gui_on']:
from gps.gui.gps_training_gui import GPSTrainingGUI # Only import if neccessary
self.gui = GPSTrainingGUI(config['common'])
self.mode = None
config['algorithm']['agent'] = self.agent
if not no_algorithm:
self.algorithm = config['algorithm']['type'](config['algorithm'])
self.algorithm._data_files_dir = self._data_files_dir
if hasattr(self.algorithm, 'policy_opt'):
self.algorithm.policy_opt._data_files_dir = self._data_files_dir
self.session_id = None
def __init__(self, config, quit_on_end=False):
"""
Initialize GPSMain
Args:
config: Hyperparameters for experiment
quit_on_end: When true, quit automatically on completion
"""
self.start_time = timeit.default_timer()
self._quit_on_end = quit_on_end
self._hyperparams = config
self._conditions = config['common']['conditions']
if 'train_conditions' in config['common']:
self._train_idx = config['common']['train_conditions']
self._test_idx = config['common']['test_conditions']
else:
self._train_idx = list(range(self._conditions))
config['common']['train_conditions'] = config['common'][
'conditions']
self._hyperparams = config
self._test_idx = self._train_idx
self._data_files_dir = config['common']['data_files_dir']
self.agent = config['agent']['type'](config['agent'])
self.data_logger = DataLogger()
self.gui = GPSTrainingGUI(
config['common']) if config['gui_on'] else None
config['algorithm']['agent'] = self.agent
self.algorithm = config['algorithm']['type'](config['algorithm'])
#CB save image of cost w/ gui
if not config['gui_on']:
self.simplePlotter = SimplePlotter(
config['common']['experiment_name'],
config['common']['data_files_dir'])
def __init__(self, config, quit_on_end=False):
"""
Initialize GPSMain
Args:
config: Hyperparameters for experiment
quit_on_end: When true, quit automatically on completion
"""
self._quit_on_end = quit_on_end
self._hyperparams = config
self._conditions = config['common']['conditions']
#self._condition = 1
if 'train_conditions' in config['common']:
#False
self._train_idx = config['common']['train_conditions']
self._test_idx = config['common']['test_conditions']
else:
self._train_idx = range(self._conditions)
config['common']['train_conditions'] = config['common'][
'conditions']
#create a new key in the dictionary common and assign the value 1
self._hyperparams = config
#reinitiallizing the hyperparameters because the config was changed
self._test_idx = self._train_idx
#getting hte train index again
self._data_files_dir = config['common']['data_files_dir']
#getting the data file path from which is stored in the common dic
self.agent = config['agent']['type'](config['agent'])
#here it creat the object from the agent directory
#print(self.agent,'self.agent')
self.data_logger = DataLogger()
#here the gui files leads to the
self.gui = GPSTrainingGUI(
config['common']) if config['gui_on'] else None
#again with they change the config file now adding object to the dic
config['algorithm']['agent'] = self.agent
self.algorithm = config['algorithm']['type'](config['algorithm'])
def __init__(self, config, quit_on_end=False):
"""
Initialize GPSMain
Args:
config: Hyperparameters for experiment
quit_on_end: When true, quit automatically on completion
"""
# This controls whether there is a learned reset or not
# There is a learned reset when special_reset = True
try:
self.special_reset = config['agent']['special_reset']
except:
self.special_reset = False
self._quit_on_end = quit_on_end
self._hyperparams = config
self._conditions = config['common']['conditions']
# There's going to be as many reset conditions as conditions
self._reset_conditions = self._conditions
if 'train_conditions' in config['common']:
self._train_idx = config['common']['train_conditions']
self._test_idx = config['common']['test_conditions']
else:
self._train_idx = range(self._conditions)
config['common']['train_conditions'] = config['common'][
'conditions']
self._hyperparams = config
self._test_idx = self._train_idx
self._data_files_dir = config['common']['data_files_dir']
self.agent = config['agent']['type'](config['agent'])
self.data_logger = DataLogger()
self.gui = GPSTrainingGUI(
config['common']) if config['gui_on'] else None
config['algorithm']['agent'] = self.agent
self.algorithm = config['algorithm']['type'](config['algorithm'])
import tensorflow as tf
try:
with tf.variable_scope(
'reset'): # to avoid variable naming conflicts
# Gonna make the algorithm for the reset ones as well
self.reset_algorithm = config['reset_algorithm']['type'](
config['algorithm'])
except:
pass
self.saved_algorithm = copy.deepcopy(
self.algorithm) # Save this newly initialized alg
# If you want to warm start the algorithm BADMM with learned iLQG controllers
self.diff_warm_start = True
# If you want to warm start the neural network with some pretrained network
self.nn_warm_start = False
# The following variables determine the pretrained network path
# Change these if you want to take the other policy type
attention, structure = 'time', 'mlp'
self.policy_path = os.path.join(
self._data_files_dir,
os.path.join('{}_{}'.format(attention, structure), 'policy'))
try:
self.old_policy_opt = copy.deepcopy(self.algorithm.policy_opt)
except:
pass
pdb.set_trace()