class LinearRBF(Policy):
'''
RBF features
'''
def __init__(self, state_dim, action_dim, number_of_features):
Policy.__init__(self, state_dim, action_dim)
self.rbf_feature = RBFSampler(gamma=25.,
n_components=number_of_features)
self.rbf_feature.fit(np.random.randn(action_dim, state_dim))
def set_theta(self, theta):
self.theta = theta
def get_action(self, state):
features = self.rbf_feature.transform(state.reshape(1, -1))
action = features @ self.theta[:-self.action_dim].reshape(
-1, self.action_dim)
action = action + self.theta[-self.action_dim:]
return action.reshape(-1)
def get_number_of_parameters(self):
return self.rbf_feature.get_params().get(
"n_components") * self.action_dim + self.action_dim
