def load(self, output_folder=None, output_filename=None):
'''
Loads the class form disk
'''
if not hasattr(self, 'registered_types'):
self.registered_types = set()
if not hasattr(self, 'registered_keys'):
self.registered_keys = set()
output_folder = utils.get_output_dir(
) if output_folder is None else output_folder
[output_filename,
self.filename] = utils.sync_output_filename(output_filename,
self.filename, '.zip')
path = os.path.join(output_folder, output_filename)
# append the zip extension
if not path.endswith('.zip'):
path = path + '.zip'
try:
with open(path, 'rb') as f:
utils.print_with_stamp('Loading state from %s' % (path),
self.name)
state = t_load(f)
self.set_instance_state(state)
self.state_changed = False
except IOError as err:
utils.print_with_stamp('Unable to load state from %s' % (path),
self.name)
print(err)
return False
return True
def save(self, output_folder=None, output_filename=None):
'''
Serializes the class using the theano pickling utility function, and saves it to disk
'''
sys.setrecursionlimit(100000)
output_folder = utils.get_output_dir(
) if output_folder is None else output_folder
[output_filename,
self.filename] = utils.sync_output_filename(output_filename,
self.filename, '.zip')
if self.state_changed or output_folder is not None or output_filename is not None:
# check if output_folder exists, create it if necessary.
if not os.path.exists(output_folder):
try:
os.makedirs(output_folder)
except OSError:
utils.print_with_stamp(
'Unable to create the directory: %s' % (output_folder),
self.name)
raise
# construct file path
path = os.path.join(output_folder, output_filename)
# append the zip extension
if not path.endswith('.zip'):
path = path + '.zip'
with open(path, 'wb') as f:
utils.print_with_stamp('Saving state to %s' % (path),
self.name)
t_dump(self.get_instance_state(), f, 2)
os.system('chmod 666 %s' % (path))
self.state_changed = False
parser = argparse.ArgumentParser()
parser.add_argument('-e',
'--exp',
type=int,
default=8,
help='id of experiment to run')
parser.add_argument('-n', '--name', type=str, help='experiment name')
parser.add_argument(
'-H',
'--horizon',
type=int,
help='steps for control horizon (length of trials in time steps)')
parser.add_argument('-o',
'--output_folder',
type=str,
default=utils.get_output_dir(),
help='where to save the results of the experiment')
parser.add_argument('-r',
'--render',
action='store_true',
help='whether to call env.render')
parser.add_argument(
'-d',
'--debug_plot',
type=int,
default=0,
help='whether to plot rollouts and other debugging information')
parser.add_argument(
'-k',
'--kwarg',
nargs=2,
import os
import numpy as np
import kusanagi.ghost.regression as kreg
from kusanagi import utils
from kusanagi.shell.cartpole import default_params#, CartpoleDraw
from kusanagi.shell.plant import SerialPlant
from kusanagi.ghost.algorithms.PILCO import PILCO, MC_PILCO
from kusanagi.ghost.control import NNPolicy
from kusanagi.utils import plot_results
#np.random.seed(31337)
np.set_printoptions(linewidth=500)
if __name__ == '__main__':
# setup output directory
utils.set_output_dir(os.path.join(utils.get_output_dir(),'cartpole_serial'))
J = 4 # number of random initial trials
N = 100 # learning iterations
learner_params = default_params()
# initialize learner
learner_params['dynmodel_class'] = kreg.SSGP_UI
learner_params['params']['dynmodel']['n_inducing'] = 100
learner_params['plant_class'] = SerialPlant
learner_params['params']['plant']['maxU'] = np.array(learner_params['params']['policy']['maxU'])*1.0/0.4
learner_params['params']['plant']['state_indices'] = [0,2,3,1]
learner_params['params']['plant']['baud_rate'] = 4000000
learner_params['params']['plant']['port'] = '/dev/ttyACM0'
#learner_params['min_method'] = 'ADAM'
#learner_params['dynmodel_class'] = NN
if val is None:
attr1, attr_next = attr.split('.')
next_mod = getattr(obj, attr1)
#print next_mod
if attr == attr1 or next_mod is None:
raise AttributeError('Can\'t find %s in module %s' % (obj, attr))
else:
return recursive_getattr(next_mod, attr_next)
else:
return val
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'-d', '--dataset_folder', type=str, default=utils.get_output_dir(),
help='where to load the results of the experiment')
parser.add_argument(
'-r', '--render', action='store_true',
help='whether to call env.render')
parser.add_argument(
'-k', '--kwarg', nargs=2, action='append', default=[],
help='additional arguments for the experiment [name value]')
parser.add_argument(
'-e', '--env', type=str, default='cartpole.Cartpole',
help='environment from kusanagi.shell')
parser.add_argument(
'-c', '--cost', type=str, default='cartpole.cartpole_loss',
help='cost funciton from kusanagi.shell')
parser.add_argument(
'-p', '--policy_class', type=str, default='NNPolicy',
return scenario_params, pol, dyn, learner_setup
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'-e', '--exp', type=int, default=8,
help='id of experiment to run')
parser.add_argument(
'-n', '--name', type=str,
help='experiment name')
parser.add_argument(
'-H', '--horizon', type=int,
help='steps for control horizon (length of trials in time steps)')
parser.add_argument(
'-o', '--output_folder', type=str, default=utils.get_output_dir(),
help='where to save the results of the experiment')
parser.add_argument(
'-r', '--render', action='store_true',
help='whether to call env.render')
parser.add_argument(
'-d', '--debug_plot', type=int, default=0,
help='whether to plot rollouts and other debugging information')
parser.add_argument(
'-k', '--kwarg', nargs=2, action='append', default=[],
help='additional arguments for the experiment [name value]')
args = parser.parse_args()
e_id = args.exp
kwargs = dict(args.kwarg)
# prepare experiment parameters
