The SARSA algorithm with linear function approximation is utilized to update policies θ E and θ D (Sutton and Barto, 1998).
the objective of training is to learn an optimal value function Q(s, a) (i.e., the expected cu-  (s, a), where φ(s, a) is a feature vector and θ is a weight updated during training.