In this study, the question in mind is the effect of employing multiple Reinforcement Learning (RL) agents to tackle the solution of a simple game. Although the game on its own may seem simple to play, the approach we are trying to develop and investigate in this work is the possibility of transferring the learning between RL agents. Transfer learning is a big and promising approach that attempts at cutting the training time of RL agents, especially if the learning that may be transferred is imported from a field similar, or possibly different, to the problem that is being studied. In this study, we attempt at targeting the play of one game from different angles or starting points using several agents in the hope of optimizing the learning when one of these agents may have a better chance of finding the winning strategy. After that, this knowledge should be induced into future RL agents attempting to play the same game.