Crowd Simulation is an area of research that is present in several disciplines and industries. Even though the visualization of crowds is an important subject, the behavior behind it helps to make it believable. Behavioral modeling can be a te- dious task because the agents have to mimic the complexity of human reactions to situations. In this work, we propose an alternative model for crowd simulation of pedestrian movements using Reinforcement Learning methods for low level deci- sions. Taking the approach of microscopic models, we trained an agent to reach a goal point while avoiding obstacles that might be on its way, and trying to follow a coherent path during the walk. Once one agent has learned, its knowledge is passed to the rest of the members of the crowd by sharing the resulting Q-Table, expecting the individual behavior and interactions to lead to a crowd behavior. We presented states sets, an action set and reward functions general enough to adapt to different environments, allowing us to use the same knowledge in different scenario settings.