def __init__(self, state_dim, control_dim, num_basis_functions, max_action=None):
MGPR.__init__(self,
np.random.randn(num_basis_functions, state_dim),
0.1*np.random.randn(num_basis_functions, control_dim)
)
for model in self.models:
model.kern.variance = 1.0
model.kern.variance.trainable = False
self.max_action = max_action
def __init__(self, X, Y, num_induced_points=None, controller=None,
reward=None, m_init=None, S_init=None, name=None, debug=False):
# super(PILCO, self).__init__(name)
if not num_induced_points: # num_induced_points ?
self.mgpr = MGPR(X, Y)
else:
self.mgpr = SMGPR(X, Y, num_induced_points)
self.state_dim = Y.shape[1]
self.control_dim = X.shape[1] - Y.shape[1]
self.sess = gpflow.get_default_session()
if debug:
self.sess = tf_debug.LocalCLIDebugWrapperSession(self.sess)
# self.sess.run(tf.global_variables_initializer())
if controller is None: # the policy - to change
print("controller cannot be None")
else:
self.controller = controller
if reward is None: # reward function
self.reward = Reward()
else:
self.reward = reward
if m_init is None or S_init is None:
# If the user has not provided an initial state for the rollouts,
# then define it as the first state in the dataset.
self.m_init = X[0:1, 0:self.state_dim]
self.S_init = np.diag(np.ones(self.state_dim) * 0.1) # variance
else:
self.m_init = m_init
self.S_init = S_init
def __init__(self, X, Y, horizon=30, m_init=None, S_init=None):
self.mgpr = MGPR(X, Y)
self.state_dim = Y.shape[1]
self.control_dim = X.shape[1] - Y.shape[1]
self.horizon = horizon
self.controller = LinearController(self.state_dim, self.control_dim)
self.reward = ExponentialReward(self.state_dim)
if m_init is None or S_init is None:
# default initial state for the rollouts is the first state in the dataset.
self.m_init = X[0:1, 0:self.state_dim]
self.S_init = np.diag(np.ones(self.state_dim) * 0.1)
else:
self.m_init = m_init
self.S_init = S_init
self.m_init = torch.tensor(self.m_init, dtype=torch.float32)
self.S_init = torch.tensor(self.S_init, dtype=torch.float32)
self.optimizer = torch.optim.Adam(self.controller.parameters())
def __init__(self,
X,
Y,
num_induced_points=None,
horizon=100,
controller=None,
reward=None,
m_init=None,
S_init=None,
name=None):
super(PILCO, self).__init__(name)
self.mgpr = MGPR(X, Y)
self.state_dim = Y.shape[1]
self.control_dim = X.shape[1] - Y.shape[1]
self.horizon = horizon
self.controller = controller
self.reward = reward
self.m_init = X[0:1, 0:self.state_dim]
self.S_init = np.diag(np.ones(self.state_dim) * 0.1)
self.optimizer = None
class PILCO:
def __init__(self, X, Y, horizon=30, m_init=None, S_init=None):
self.mgpr = MGPR(X, Y)
self.state_dim = Y.shape[1]
self.control_dim = X.shape[1] - Y.shape[1]
self.horizon = horizon
self.controller = LinearController(self.state_dim, self.control_dim)
self.reward = ExponentialReward(self.state_dim)
if m_init is None or S_init is None:
# default initial state for the rollouts is the first state in the dataset.
self.m_init = X[0:1, 0:self.state_dim]
self.S_init = np.diag(np.ones(self.state_dim) * 0.1)
else:
self.m_init = m_init
self.S_init = S_init
self.m_init = torch.tensor(self.m_init, dtype=torch.float32)
self.S_init = torch.tensor(self.S_init, dtype=torch.float32)
self.optimizer = torch.optim.Adam(self.controller.parameters())
def optimize_models(self, maxiter=200):
'''
Optimize GP models
'''
self.mgpr.optimize(max_iter=maxiter)
self.mgpr.eval()
# print learned dynamics model parameters
lengthscales = {}
variances = {}
noises = {}
i = 0
for model in self.mgpr.models:
lengthscales[
'GP' +
str(i)] = model.covar_module.base_kernel.lengthscale.detach(
).numpy().ravel()
variances['GP' + str(i)] = np.array(
[model.covar_module.outputscale.item()])
noises['GP' + str(i)] = np.array([model.likelihood.noise.item()])
i += 1
print('-----Learned models------')
pd.set_option('precision', 3)
print('---Lengthscales---')
print(pd.DataFrame(data=lengthscales))
print('---Variances---')
print(pd.DataFrame(data=variances))
print('---Noises---')
print(pd.DataFrame(data=noises))
def optimize_policy(self, maxiter=50):
'''
Optimize controller's parameter's
'''
self.mgpr.eval()
start = time.time()
for i in range(maxiter):
self.optimizer.zero_grad()
reward = self.compute_reward() # policy evaluation
loss = -reward
loss.backward() # policy improvement by policy gradient
self.optimizer.step()
end = time.time()
print(
"Controller's optimization: done in %.1f seconds with reward=%.3f."
% (end - start, self.compute_reward()))
def compute_action(self, x_m):
x_m = torch.tensor(x_m, dtype=torch.float32)
x_s = torch.zeros((self.state_dim, self.state_dim),
dtype=torch.float32)
return self.controller((x_m, x_s))[0].detach().numpy()
def predict(self, m_x, s_x, n):
'''
predict n steps with learned model
'''
reward = 0
for _ in range(n):
m_x, s_x = self.propagate(m_x, s_x)
reward += self.reward.compute_reward(m_x, s_x)
return m_x, s_x, reward
def propagate(self, m_x, s_x):
'''
propagate from one state distribution to the next one with controller and GP models
'''
# from state x to control u
m_u, s_u, c_xu = self.controller((m_x, s_x))
# joint distribution of x and u
m = torch.cat([m_x, m_u], axis=1)
s1 = torch.cat([s_x, s_x @ c_xu], axis=1)
s2 = torch.cat([(s_x @ c_xu).T, s_u], axis=1)
s = torch.cat([s1, s2], axis=0)
# go to next state by moment matching
M_dx, S_dx, C_dx = self.mgpr.predict_on_noisy_inputs(m, s)
M_x = M_dx + m_x
S_x = S_dx + s_x + s1 @ C_dx + C_dx.T @ s1.T
M_x.reshape(1, self.state_dim)
S_x.reshape(self.state_dim, self.state_dim)
return M_x, S_x
def compute_reward(self):
reward = self.predict(self.m_init, self.S_init, self.horizon)[2]
return reward
def __init__(self, X, Y, num_induced_points, name=None):
gpflow.Parameterized.__init__(self, name)
self.num_induced_points = num_induced_points
MGPR.__init__(self, X, Y, name)
class PILCO(gpflow.models.Model):
def __init__(self,
X,
Y,
num_induced_points=None,
horizon=100,
controller=None,
reward=None,
m_init=None,
S_init=None,
name=None):
super(PILCO, self).__init__(name)
self.mgpr = MGPR(X, Y)
self.state_dim = Y.shape[1]
self.control_dim = X.shape[1] - Y.shape[1]
self.horizon = horizon
self.controller = controller
self.reward = reward
self.m_init = X[0:1, 0:self.state_dim]
self.S_init = np.diag(np.ones(self.state_dim) * 0.1)
self.optimizer = None
@gpflow.name_scope('likelihood')
def _build_likelihood(self):
# This is for tuning controller's parameters
reward = self.predict(self.m_init, self.S_init, self.horizon)[2]
return reward
def optimize_models(self, maxiter=200, restarts=1):
'''
Optimize GP models
'''
self.mgpr.optimize(restarts=restarts)
# Print the resulting model parameters
lengthscales = {}
variances = {}
noises = {}
i = 0
for model in self.mgpr.models:
lengthscales['GP' + str(i)] = model.kern.lengthscales.value
variances['GP' + str(i)] = np.array([model.kern.variance.value])
noises['GP' + str(i)] = np.array([model.likelihood.variance.value])
i += 1
print('-----Learned models------')
pd.set_option('precision', 3)
print('---Lengthscales---')
print(pd.DataFrame(data=lengthscales))
print('---Variances---')
print(pd.DataFrame(data=variances))
print('---Noises---')
print(pd.DataFrame(data=noises))
def optimize_policy(self, maxiter=30, restarts=0):
'''
Optimize controller's parameter's
'''
start = time.time()
if not self.optimizer:
self.optimizer = gpflow.train.ScipyOptimizer(method="L-BFGS-B")
start = time.time()
self.optimizer.minimize(self, maxiter=maxiter)
end = time.time()
print(
"Controller's optimization: done in %.1f seconds with reward=%.3f."
% (end - start, self.compute_reward()))
session = self.optimizer._model.enquire_session(None)
start = time.time()
self.optimizer._optimizer.minimize(
session=session,
feed_dict=self.optimizer._gen_feed_dict(self.optimizer._model,
None),
step_callback=None)
end = time.time()
print(
"Controller's optimization: done in %.1f seconds with reward=%.3f."
% (end - start, self.compute_reward()))
best_parameters = self.read_values(session=session)
self.assign(best_parameters)
@gpflow.autoflow((float_type, [None, None]))
def compute_action(self, x_m):
return self.controller.compute_action(
x_m, tf.zeros([self.state_dim, self.state_dim], float_type))[0]
def predict(self, m_x, s_x, n):
loop_vars = [
tf.constant(0, tf.int32), m_x, s_x,
tf.constant([[0]], float_type)
]
_, m_x, s_x, reward = tf.while_loop(
# Termination condition
lambda j, m_x, s_x, reward: j < n,
# Body function
lambda j, m_x, s_x, reward:
(j + 1, *self.propagate(m_x, s_x),
tf.add(reward,
self.reward.compute_reward(m_x, s_x)[0])),
loop_vars)
return m_x, s_x, reward
def propagate(self, m_x, s_x):
m_u, s_u, c_xu = self.controller.compute_action(m_x, s_x)
m = tf.concat([m_x, m_u], axis=1)
s1 = tf.concat([s_x, s_x @ c_xu], axis=1)
s2 = tf.concat([tf.transpose(s_x @ c_xu), s_u], axis=1)
s = tf.concat([s1, s2], axis=0)
M_dx, S_dx, C_dx = self.mgpr.predict_on_noisy_inputs(m, s)
M_x = M_dx + m_x
S_x = S_dx + s_x + s1 @ C_dx + tf.matmul(
C_dx, s1, transpose_a=True, transpose_b=True)
# While-loop requires the shapes of the outputs to be fixed
M_x.set_shape([1, self.state_dim])
S_x.set_shape([self.state_dim, self.state_dim])
return M_x, S_x
@gpflow.autoflow()
def compute_reward(self):
return self._build_likelihood()