def initialize_objective(self):
H, ds, da = self.horizon, self.ds, self.da
if self.time_varying:
A = T.concatenate([T.eye(ds), T.zeros([ds, da])], -1)
self.A = T.variable(A[None] + 1e-2 * T.random_normal([H - 1, ds, ds + da]))
self.Q_log_diag = T.variable(T.random_normal([H - 1, ds]) + 1)
self.Q = T.matrix_diag(T.exp(self.Q_log_diag))
else:
A = T.concatenate([T.eye(ds), T.zeros([ds, da])], -1)
self.A = T.variable(A + 1e-2 * T.random_normal([ds, ds + da]))
self.Q_log_diag = T.variable(T.random_normal([ds]) + 1)
self.Q = T.matrix_diag(T.exp(self.Q_log_diag))
def activate(self, X):
import tensorflow as tf
if self.cov_type == 'diagonal':
sigma, mu = T.split(X, 2, axis=-1)
sigma = T.matrix_diag(log1pexp(sigma))
return stats.Gaussian([sigma, mu], parameter_type='regular')
raise Exception("Undefined covariance type: %s" % self.cov_type)
N, H, ds, da = 1, 2, 4, 2
# random rotation for state-state transition
A = np.zeros([H - 1, ds, ds])
for t in range(H - 1):
theta = 0.5 * np.pi * np.random.rand()
rot = np.array([[np.cos(theta), -np.sin(theta)],
[np.sin(theta), np.cos(theta)]])
out = np.zeros((ds, ds))
out[:2, :2] = rot
q = np.linalg.qr(np.random.randn(ds, ds))[0]
A[t] = q.dot(out).dot(q.T)
A = T.constant(A, dtype=T.floatx())
B = T.constant(0.1 * np.random.randn(H - 1, ds, da), dtype=T.floatx())
Q = T.matrix_diag(
np.random.uniform(low=0.9, high=1.1, size=[H - 1, ds]).astype(np.float32))
prior = stats.Gaussian([T.eye(ds), T.zeros(ds)])
p_S = stats.Gaussian([
T.eye(ds, batch_shape=[N, H]),
T.constant(np.random.randn(N, H, ds), dtype=T.floatx())
])
potentials = stats.Gaussian.unpack(
p_S.get_parameters('natural')) + [p_S.log_z()]
actions = T.constant(np.random.randn(N, H, da), dtype=T.floatx())
lds = stats.LDS(((A, B, Q), prior, potentials, actions))
sess = T.interactive_session()
np.set_printoptions(suppress=True,