  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.
