Abstract: Path smoothness is often overlooked in path imitation learning from expert demonstrations. In this paper, we introduce a novel learning method, termed deep angular A$^\ast$ (DAA$^\ast$), by incorporating the proposed path angular freedom (PAF) into A$^\ast$ to improve path similarity through adaptive path smoothness. The PAF aims to explore the effect of move angles on path node expansion by finding the trade-off between their minimum and maximum values, allowing for high adaptiveness for imitation learning. DAA$^\ast$ improves path optimality by closely aligning with the reference path through joint optimization of path shortening and smoothing, which correspond to heuristic distance and PAF, respectively. Throughout comprehensive evaluations on 7 datasets, including 4 maze datasets, 2 video-game datasets, and a real-world drone-view dataset containing 2 scenarios, we demonstrate remarkable improvements of our DAA$^\ast$ over neural A$^\ast$ in path similarity between the predicted and reference paths with a shorter path length when the shortest path is plausible, improving by **9.0\% SPR**, **6.9\% ASIM**, and **3.9\% PSIM**. Furthermore, when jointly learning pathfinding with both path loss and path probability map loss, DAA$^\ast$ significantly outperforms the state-of-the-art TransPath by **6.7\% SPR**, **6.5\% PSIM**, and **3.7\% ASIM**. We also discuss the minor trade-off between path optimality and search efficiency where applicable.