One of the major drawbacks of RL is the low sample efficiency of the learning algorithms. In many cases domain expertise can help to mitigate this effect. Teacher-Student framework is one such paradigm, where a more experienced agent (teacher) upon being queried helps to accelerate the student's learning by providing advice on the action to take in a given state. Real world teachers not only provide the action to take in a given state but also provide a more informative signal using the synthesis of knowledge they may have gained with experience. With this motivation, we propose a richer advising framework where the teacher augments the student's knowledge by also providing the expected long term reward of following that action. The student can then use this value to steadily guide its Q-Network in the correct direction which can lead to a quicker convergence. To help student relive the advices received throughout its learning, we introduce an additional memory called the Advice Replay Memory (ARM). Results show that a student following our approach (a) is able to exploit the environment better, and (b) has a steeper learning curve. © 2019 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.