import tensorflow as tf

        # action_prob = self.printn('action_prob shape: ', action_prob)
        # q_values = self.printn('q_values shape: ', q_values)
        # beta = self.printn('beta shape: ', beta)

        # ha(s): eta * (\varphi(s)^T * K^T * \Sigma^{-1} + W_{sa}) + wa(s))
        ha = tf.matmul(varphis, param_eta * tf.matmul(Kt, prec) + Wsa) + wa

        # hss(s): eta * (\varphi(s)^T * K^T * \Sigma^{-1} * K * \varphi(s))
        varphisKt = tf.matmul(varphis, Kt)
        hss = param_eta * tf.reduce_sum(tf.matmul(varphisKt, prec) * varphisKt, axis=1)
