The following paper is a reproducibility report for "Path Planning using Neural A* Search" published in ICML2 2021 as part of the ML Reproducibility Challenge 2021. The original paper proposes the Neural A* planner, and claims it achieves an optimal balance between the reduction of node expansions and path accuracy. We verify this claim by reimplementing the model in a different framework and reproduce the data published in the original paper. We have also provided a code-flow diagram to aid comprehension of the code structure. As extensions to the original paper, we explore the effects of (1) generalizing the model by training it on a shuffled dataset, (2) introducing dropout, (3) implementing empirically chosen hyperparameters as trainable parameters in the model, (4) altering the network model to Generative Adversarial Networks (GANs) to introduce stochasticity, (5) modifying the encoder from Unet to Unet++, (6) incorporating cost maps obtained from the Neural A* module in other variations of A* search.

本文为2021年ICML2“机器学习可重复性挑战赛”之一“Path Planning using Neural A* Search”的可重复性报告，基于原始论文提出的神经A*规划器，通过重新实现模型并复现原始论文中的数据，验证了该模型在节点扩展的减少和路径准确性之间达到最佳平衡的说法，并提供了代码流程图以帮助理解；此外，本文还探索了几种扩展合优化模型的方法，如：（1）在混洗数据集上训练模型的泛化能力；（2）引入dropout等措施；（3）使用实证选择的超参数作为模型的可训练值；（4）将网络模型改为生成对抗网络（GAN）以引入随机性；（5）将Unet的编码器修改为Unet++；（6）在其他A *搜索的变异中，融入从神经A *模块获得的成本地图。

神经网络A*搜索的路径规划再现性报告