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