Resorting to certain heuristic functions to guide the search, the computational efficiency of prevailing path planning algorithms, such as A*, D*, and their variants, is solely determined by how well the heuristic function approximates the true path cost. In this study, we propose a novel approach to learning heuristic functions using a deep neural network (DNN) to improve the computational efficiency. Even though DNNs have been widely used for object segmentation, natural language processing, and perception, their role in helping to solve path planning problems has not been well investigated. This work shows how DNNs can be applied to path planning and what kind of loss functions are suitable for learning such a heuristic. Our preliminary results show that an appropriately designed and trained DNN can learn a heuristic that effectively guides prevailing path planning algorithms.