In the field of robot navigation, a number of different approaches have been proposed. One of these is Hybrid Fuzzy A* (HFA), which uses the A* algorithm to determine the long term path from the robot to some target, and fuzzy logic to move the robot to each waypoint along the path. This algorithm has been shown to be fast and effective in simulation, however A* is limited in the variables it can consider and the challenges it can be applied to. We propose replacing A* with Q-learning, which does not suffer from these limitations. We demonstrate the ability of Hybrid Fuzzy Q-Learning (HFQL) to navigate a robot to a given target and then apply the algorithm to a different challenge where the robot needs to balance reaching the target quickly against picking up as many subgoals as possible.