We interleave sampling based motion planning methods with pruning ideas from
minimum spanning tree algorithms to develop a new approach for solving a
Multi-Goal Path Finding (MGPF) problem in high dimensional spaces. The approach
alternates between sampling points from selected regions in the search space
and de-emphasizing regions that may not lead to good solutions for MGPF. Our
approach provides an asymptotic, 2-approximation guarantee for MGPF. We also
present extensive numerical results to illustrate the advantages of our
proposed approach over uniform sampling in terms of the quality of the
solutions found and computation speed.

本文提出了一种新的方法，在高维空间中交替在选择的区域中采样点并减弱不能导致多目标路径查找问题的良好解决方案的区域。实验结果证明该方法在解决问题质量和计算速度方面优于一般均匀采样方法。

高维多目标路径规划的取样和剪枝：信息斯坦纳树

Informed Steiner Trees: Sampling and Pruning for Multi-Goal Path Finding in High Dimensions

Exploration is an extremely challenging problem in reinforcement learning,
especially in high dimensional state and action spaces and when only sparse
rewards are available. Effective representations can indicate which components
of the state are task relevant and thus reduce the dimensionality of the space
to explore. In this work, we take a representation learning viewpoint on
exploration, utilizing prior experience to learn effective latent
representations, which can subsequently indicate which regions to explore.
Prior experience on separate but related tasks help learn representations of
the state which are effective at predicting instantaneous rewards. These
learned representations can then be used with an entropy-based exploration
method to effectively perform exploration in high dimensional spaces by
effectively lowering the dimensionality of the search space. We show the
benefits of this representation for meta-exploration in a simulated object
pushing environment.

通过先前的经验来学习有效的隐藏表示，以指示探索的区域，这对高维空间中的探索尤其有益。

学习潜在状态表示以加速探索

Learning latent state representation for speeding up exploration

We present a new approach for efficient approximate nearest neighbor (ANN)
search in high dimensional spaces, extending the idea of Product Quantization.
We propose a two-level product and vector quantization tree that reduces the
number of vector comparisons required during tree traversal. Our approach also
includes a novel highly parallelizable re-ranking method for candidate vectors
by efficiently reusing already computed intermediate values. Due to its small
memory footprint during traversal, the method lends itself to an efficient,
parallel GPU implementation. This Product Quantization Tree (PQT) approach
significantly outperforms recent state of the art methods for high dimensional
nearest neighbor queries on standard reference datasets. Ours is the first work
that demonstrates GPU performance superior to CPU performance on high
dimensional, large scale ANN problems in time-critical real-world applications,
like loop-closing in videos.

本研究提出基于 Product Quantization 的两层向量量化树的 ANN 搜索新方法，在 GPU 实现方面取得了比 CPU 更高的性能表现，适用于视频中循环关闭等对时间敏感的高维问题。