Before revision:  It offers novel intuitive insights on analysis of Q-learning mainly based on control theoretic frameworks. By filling the gap between both domains in a synergistic way, this approach can potentially facilitate further progress in each field .
After revision:  It offers novel intuitive insights on analysis of Q-learning . We further illustrate and validate the analysis through numerical simulations .