def build_dataset(idims=9,odims=6,angi=[],f=test_func1,n_train=500,n_test=50, input_noise=0.01, output_noise=0.01,rand_seed=None):
if rand_seed is not None:
np.random.seed(rand_seed)
# ================== train dataset ==================
# sample training points
x_train = 15*(np.random.rand(n_train,idims) - 0.25)
# generate the output at the training points
y_train = np.empty((n_train,odims))
for i in range(odims):
y_train[:,i] = (i+1)*f(x_train) + output_noise*(np.random.randn(n_train))
x_train = utils.gTrig_np(x_train, angi)
# ================== test dataset ==================
# generate testing points
#kk = input_noise*convolve2d(np.array([[1,2,3,2,1]]),np.array([[1,2,3,2,1]]).T)/9.0;
#s_test = convolve2d(np.eye(idims),kk,'same')
s_test = input_noise*np.eye(idims)
s_test = np.tile(s_test,(n_test,1)).reshape(n_test,idims,idims)
x_test = 120*(np.random.rand(n_test,idims) - 0.5)
# generate the output at the test points
y_test = np.empty((n_test,odims))
for i in range(odims):
y_test[:,i] = (i+1)*f(x_test)
if len(angi)>0:
x_test,s_test = utils.gTrig2_np(x_test,s_test, angi, idims)
return (x_train,y_train),(x_test,y_test,s_test)
def init_params(self):
H_steps = int(np.ceil(self.H / self.dt))
self.state_changed = False
# set random (uniform distribution) controls
u = self.maxU * (2 * np.random.random((H_steps, len(self.maxU))) - 1)
self.u_nominal = theano.shared(u)
# intialize the nominal states to the appropriate size
m0, S0 = utils.gTrig2_np(
np.array(self.m0)[None, :],
np.array(self.S0)[None, :, :], self.angle_dims, len(self.m0))
self.triu_indices = np.triu_indices(m0.size)
z0 = np.concatenate([m0.flatten(), S0[0][self.triu_indices]])
z = np.tile(z0, (H_steps, 1))
self.z_nominal = theano.shared(z)
# initialize the open loop and feedback matrices
I = np.zeros((H_steps, len(self.maxU)))
L = np.zeros((H_steps, len(self.maxU), z0.size))
self.I = theano.shared(I)
self.L = theano.shared(L)
# set a meaningful filename
self.filename = self.name + '_' + str(len(self.m0)) + '_' + str(
len(self.maxU))
def default_params():
# setup learner parameters
angi = [0]
x0 = np.array([0, 0])
S0 = np.eye(len(x0)) * (0.2**2)
p0 = utils.distributions.Gaussian(x0, S0)
x0a, S0a = utils.gTrig2_np(x0[None, :],
np.array(S0)[None, :, :], angi, len(x0))
# plant parameters
plant_params = {}
plant_params['dt'] = 0.1
plant_params['pole_length'] = 1.0
plant_params['pole_mass'] = 1.0
plant_params['friction'] = 0.01
plant_params['gravity'] = 9.82
plant_params['state0_dist'] = p0
plant_params['noise_dist'] = utils.distributions.Gaussian(
np.zeros((p0.dim, )),
np.diag([0.01, 0.1])**2)
# policy parameters
policy_params = {}
policy_params['state0_dist'] = p0
policy_params['angle_dims'] = angi
policy_params['n_inducing'] = 20
policy_params['maxU'] = [2.5]
# dynamics model parameters
dynmodel_params = {}
dynmodel_params['idims'] = x0a.size + len(policy_params['maxU'])
dynmodel_params['odims'] = x0.size
dynmodel_params['n_inducing'] = 100
# cost function parameters
cost_params = {}
cost_params['angle_dims'] = angi
cost_params['target'] = [np.pi, 0]
cost_params['cw'] = 0.5
cost_params['expl'] = 0.0
cost_params['pole_length'] = plant_params['pole_length']
cost_params['loss_func'] = cost.quadratic_saturating_loss
# optimizer params
opt_params = {}
opt_params['max_evals'] = 100
opt_params['conv_thr'] = 1e-16
opt_params['min_method'] = 'L-BFGS-B'
# general parameters
params = {}
params['state0_dist'] = p0
params['angle_dims'] = angi
params['min_steps'] = int(4.0 / plant_params['dt']) # control horizon
params['max_steps'] = int(4.0 / plant_params['dt']) # control horizon
params['discount'] = 1.0 # discount factor
params['plant'] = plant_params
params['policy'] = policy_params
params['dynamics_model'] = dynmodel_params
params['cost'] = cost_params
params['optimizer'] = opt_params
return params