When learning task-oriented dialogue (TOD) agents, one can naturally utilize reinforcement learning (RL) techniques to train dialogue strategies to achieve user-specific goals. Prior works mainly focus on adopting advanced RL techniques to train the TOD agents, while the design of the reward function is not well studied. This paper aims at answering the question of how to efficiently learn and leverage a reward function for training end-to-end TOD agents. Specifically, we introduce two generalized objectives for reward-function learning, inspired by the classical learning-to-rank literature. Further, we utilize the learned reward-function to guide the training of the end-to-end TOD agent. With the proposed techniques, we achieve competitive results on the end-to-end response-generation task on the Multiwoz 2.0 dataset.