The purpose of exploration is to reduce goal-relevant uncertainty. This can be achieved by choosing to explore the parts of the environment one is most uncertain about. Humans, however, often choose to avoid uncertainty. How do humans balance approaching and avoiding uncertainty during exploration? To answer this question, we developed a task requiring participants to explore a simulated environment towards a clear goal. We compared human choices to the predictions of the optimal exploration policy and a hierarchy of simpler strategies. We found that participants generally explored the object they were more uncertain about. However, when overall uncertainty about choice options was high, participants avoided objects they were more uncertain about, learning instead about better known objects. We examined reaction times and individual differences to understand the costs and benefits of this strategy. We conclude that balancing approaching and avoiding uncertainty ameliorates the costs of exploration in a resource-rational manner.