Evolutionary Game Theory improves on this by providing a dynamics which describes how strategies evolve over time. In this paper, we discuss three learning models whose dynamics are related to the Replicator Dynamics(RD). We show how a classical Reinforcement Learning(RL) technique, i.e. Q-learning relates to the RD. This allows to better understand the learning process and it allows to determine how complex a RL model should be. More precisely, Occam’s Razor applies in the framework of games, i.e. the simplest model (Cross) suffices for learning equilibria. An experimental verification in all three models is presented.