Reinforcement learning (RL) offers a capable and intuitive structure for the
fundamental sequential decision-making problem. Despite impressive
breakthroughs, it can still be difficult to employ RL in practice in many
simple applications. In this paper, we try to address this issue by introducing
a method for designing the components of the RL environment for a given,
user-intended application. We provide an initial formalization for the problem
of RL component design, that concentrates on designing a good representation
for observation and action space. We propose a method named DeLF: Designing
Learning Environments with Foundation Models, that employs large language
models to design and codify the user's intended learning scenario. By testing
our method on four different learning environments, we demonstrate that DeLF
can obtain executable environment codes for the corresponding RL problems.

通过使用大语言模型设计和编码用户预期的学习场景，我们提出了一种名为 DeLF 的方法，用于设计强化学习环境的组件，以解决在实践中应用 RL 在许多简单应用中仍然困难的问题。我们通过在四个不同的学习环境上测试我们的方法，证明 DeLF 能够为相应的 RL 问题获得可执行的环境代码。

DeLF: 使用基础模型设计学习环境

DeLF: Designing Learning Environments with Foundation Models

We propose an attentive local feature descriptor suitable for large-scale
image retrieval, referred to as DELF (DEep Local Feature). The new feature is
based on convolutional neural networks, which are trained only with image-level
annotations on a landmark image dataset. To identify semantically useful local
features for image retrieval, we also propose an attention mechanism for
keypoint selection, which shares most network layers with the descriptor. This
framework can be used for image retrieval as a drop-in replacement for other
keypoint detectors and descriptors, enabling more accurate feature matching and
geometric verification. Our system produces reliable confidence scores to
reject false positives---in particular, it is robust against queries that have
no correct match in the database. To evaluate the proposed descriptor, we
introduce a new large-scale dataset, referred to as Google-Landmarks dataset,
which involves challenges in both database and query such as background
clutter, partial occlusion, multiple landmarks, objects in variable scales,
etc. We show that DELF outperforms the state-of-the-art global and local
descriptors in the large-scale setting by significant margins. Code and dataset
can be found at the project webpage:
this https URL .

我们提出了一种基于卷积神经网络的关注局部特征描述符 DELF，它可以作为关键点探测器和描述符的一种替代品，能够产生可靠的置信度得分，能够有效地进行图像检索。我们对 Google-Landmarks 数据集进行了测试，结果表明 DELF 在大规模情况下优于最先进的全局和局部描述符。