def main( controller_type, training_data, policy_file, gps_params = gps_params_default ):
sys.path.append( gps_params[ 'caffe_path' ] )
# create controller
if controller_type == 'cartpole':
C = controller.CartPole()
C.set_system_cost( x0=np.array( [ 0, 0, 0, 0 ] ), u0=0.0, Wx=np.eye( 4 ), Wu=1e-3 )
else:
print( 'not implemented: ' + controller_type )
x_len = C.get_x_len()
u_len = C.get_u_len()
gps_params[ 'x_len' ] = x_len
gps_params[ 'u_len' ] = u_len
# load training data
f = h5py.File( 'data.h5', 'r' )
xu_train_orig = f[ 'x' ].value
# if 'o' not in f:
# print( 'WARNING: observations not in data' )
# o_train_orig = None
# else:
# o_train_orig = f[ 'o' ]
# o_train_orig = o_train_orig[ ::gps_params[ 'resample' ] ]
o_train = None
if xu_train_orig.ndim == 2:
xu_train_orig = xu_train_orig[ :, 1: ] # TODO - at the moment 1st column is time
s = xu_train_orig.shape
xu_train_orig = xu_train_orig.reshape( (1,s[0], s[1]) )
resample_idx = range( 0, xu_train_orig.shape[1], gps_params[ 'resample' ] )
xu_train_orig = np.take( xu_train_orig, resample_idx, axis=1 )
xu_train = xu_train_orig
x_train = xu_train_orig[ :,:, :x_len ]
u_train = xu_train_orig[ :,:, x_len ] # TODO check table organisation
gps = GPS( gps_params )
training_errors = []
# loop untill convergence:
for k in range( gps_params[ 'K'] ):
print( 'running for k: {0}'.format( k ) )
# execute gps
policy, training_error = gps.train( xu_train, o_train, C.get_system_cost(), gps_params )
training_errors.append( training_error )
# run on controller and collect the data
xu_run = C.run( policy )
# merge data
xu_train = gps.merge_data( xu_train, o_train, xu_run, o_run )
def main(controller_type,
training_data,
policy_file,
gps_params=gps_params_default):
sys.path.append(gps_params['caffe_path'])
# create controller
if controller_type == 'cartpole':
C = controller.CartPole()
C.set_system_cost(x0=np.array([0, 0, 0, 0]),
u0=0.0,
Wx=np.eye(4),
Wu=1e-3)
else:
print('not implemented: ' + controller_type)
x_len = C.get_x_len()
u_len = C.get_u_len()
gps_params['x_len'] = x_len
gps_params['u_len'] = u_len
# load training data
f = h5py.File('data.h5', 'r')
xu_train_orig = f['x'].value
# if 'o' not in f:
# print( 'WARNING: observations not in data' )
# o_train_orig = None
# else:
# o_train_orig = f[ 'o' ]
# o_train_orig = o_train_orig[ ::gps_params[ 'resample' ] ]
o_train = None
if xu_train_orig.ndim == 2:
xu_train_orig = xu_train_orig[:,
1:] # TODO - at the moment 1st column is time
s = xu_train_orig.shape
xu_train_orig = xu_train_orig.reshape((1, s[0], s[1]))
resample_idx = range(0, xu_train_orig.shape[1], gps_params['resample'])
xu_train_orig = np.take(xu_train_orig, resample_idx, axis=1)
xu_train = xu_train_orig
x_train = xu_train_orig[:, :, :x_len]
u_train = xu_train_orig[:, :, x_len] # TODO check table organisation
gps = GPS(gps_params)
training_errors = []
# loop untill convergence:
for k in range(gps_params['K']):
print('running for k: {0}'.format(k))
# execute gps
policy, training_error = gps.train(xu_train, o_train,
C.get_system_cost(), gps_params)
training_errors.append(training_error)
# run on controller and collect the data
xu_run = C.run(policy)
# merge data
xu_train = gps.merge_data(xu_train, o_train, xu_run, o_run)
