This thesis studies the challenges of mobile navigation in a dynamic environment. In this thesi,s different categories of algorithms (graph-based, sample-based,...) are tested in static and dynamic environments to study their functionality, and then finally, I integrate reinforcement learning and potential field methods for mobile navigation enhancement. The final navigation approach is a hybrid approach that consists of a global planner( Hybrid Aand Dlite), a local planner( Q-learning and potential field method). The global planner provides the robot with a pre-defined path based on the given map and the local planner helps the robot to react in real-time to unexpected changes in the environment. The hybrid approach is a more stable and adaptable method, although it should be refined to ensure stability and consistency in performance.