Summary: Reinforcement Learning (RL) is a simulation-based counterpart of stochastic dynamic programming. In recent years, it has been used in solving complex Markov decision problems (MDPs). Watkins’ \(Q\)-Learning is by far the most popular RL algorithm used for solving discounted-reward MDPs. The boundedness of the iterates in \(4Q\)-Learning plays a critical role in its convergence analysis and in making the algorithm stable, which makes it extremely attractive in numerical solutions. Previous results show boundedness asymptotically in an almost sure sense. We present a new result that shows boundedness in an absolute sense under some weaker conditions for the step size. Also, our proof is based on some simple induction arguments.