PointGoal navigation has seen significant recent interest and progress,
spurred on by the Habitat platform and associated challenge. In this paper, we
study PointGoal navigation under both a sample budget (75 million frames) and a
compute budget (1 GPU for 1 day). We conduct an extensive set of experiments,
cumulatively totaling over 50,000 GPU-hours, that let us identify and discuss a
number of ostensibly minor but significant design choices -- the advantage
estimation procedure (a key component in training), visual encoder
architecture, and a seemingly minor hyper-parameter change. Overall, these
design choices to lead considerable and consistent improvements over the
baselines present in Savva et al. Under a sample budget, performance for RGB-D
agents improves 8 SPL on Gibson (14% relative improvement) and 20 SPL on
Matterport3D (38% relative improvement). Under a compute budget, performance
for RGB-D agents improves by 19 SPL on Gibson (32% relative improvement) and 35
SPL on Matterport3D (220% relative improvement). We hope our findings and
recommendations will make serve to make the community's experiments more
efficient.

本文通过大量实验研究了在预算和计算方面下的面向目标点的导航，从而确定了关键组件训练的 Advance Estimation Procedure、视觉编码器架构以及超参数改变所取得的成效，提高了在 Gibson 和 Matterport3D 上的性能表现，期望为社区的实验提供更高效的建议。

如何在有限的样本和计算资源上训练出点目标导航智能体

How to Train PointGoal Navigation Agents on a (Sample and Compute)  Budget

We propose Efficient Neural Architecture Search (ENAS), a fast and
inexpensive approach for automatic model design. In ENAS, a controller learns
to discover neural network architectures by searching for an optimal subgraph
within a large computational graph. The controller is trained with policy
gradient to select a subgraph that maximizes the expected reward on the
validation set. Meanwhile the model corresponding to the selected subgraph is
trained to minimize a canonical cross entropy loss. Thanks to parameter sharing
between child models, ENAS is fast: it delivers strong empirical performances
using much fewer GPU-hours than all existing automatic model design approaches,
and notably, 1000x less expensive than standard Neural Architecture Search. On
the Penn Treebank dataset, ENAS discovers a novel architecture that achieves a
test perplexity of 55.8, establishing a new state-of-the-art among all methods
without post-training processing. On the CIFAR-10 dataset, ENAS designs novel
architectures that achieve a test error of 2.89%, which is on par with NASNet
(Zoph et al., 2018), whose test error is 2.65%.

ENAS 提出了高效神经架构搜索方法，使用控制器在大型计算图中发现神经网络架构，以优化子图为目标进行训练，利用共享子模型参数，使用的 GPU-hours 要比所有现有的自动模型设计方法少 1000 倍，设计出实现最新技术水平的新型结构。