论文标题
[可重复性报告]使用神经a*搜索的路径计划
[Reproducibility Report] Path Planning using Neural A* Search
论文作者
论文摘要
以下论文是一份可重复性的报告，用于在ICML2 2021中发表的“使用神经A*搜索”作为ML可重复性挑战2021的一部分。原始论文提出了神经A*计划者，并声称它在降低节点扩展和准确性之间实现了最佳平衡。我们通过在不同的框架中重新实现模型并重现原始论文中发布的数据来验证这一主张。我们还提供了一个代码流图来帮助理解代码结构。作为对原始论文的扩展，我们探讨了（1）通过在洗牌数据集中训练该模型的效果，（2）引入辍学，（3）实施经验选择的超参数为模型中的可训练参数，（4）将网络模型从网络中转移到生成的对手网络（GANS），以将其引入conterial+conterifuce inter inter（gans）（5）contection（5）conterife and contection（5） （6）合并从神经A*模块获得的成本图中的其他变化。
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.
