Recently, a simple but powerful language for expressing and learning general
policies and problem decompositions (sketches) has been introduced in terms of
rules defined over a set of Boolean and numerical features. In this work, we
consider three extensions of this language aimed at making policies and
sketches more flexible and reusable: internal memory states, as in finite state
controllers; indexical features, whose values are a function of the state and a
number of internal registers that can be loaded with objects; and modules that
wrap up policies and sketches and allow them to call each other by passing
parameters. In addition, unlike general policies that select state transitions
rather than ground actions, the new language allows for the selection of such
actions. The expressive power of the resulting language for policies and
sketches is illustrated through a number of examples.

该研究论文介绍了一种简单但强大的语言，用于表达和学习一般策略和问题分解（草图），并通过引入内部记忆状态、指示性特征和模块化等扩展使得这种语言更加灵活和可重用。

关于策略复用：一种表示和执行普通调用其他策略的表达性语言

On Policy Reuse: An Expressive Language for Representing and Executing  General Policies that Call Other Policies

Video steganography is the art of unobtrusively concealing secret data in a
cover video and then recovering the secret data through a decoding protocol at
the receiver end. Although several attempts have been made, most of them are
limited to low-capacity and fixed steganography. To rectify these weaknesses,
we propose a Large-capacity and Flexible Video Steganography Network (LF-VSN)
in this paper. For large-capacity, we present a reversible pipeline to perform
multiple videos hiding and recovering through a single invertible neural
network (INN). Our method can hide/recover 7 secret videos in/from 1 cover
video with promising performance. For flexibility, we propose a
key-controllable scheme, enabling different receivers to recover particular
secret videos from the same cover video through specific keys. Moreover, we
further improve the flexibility by proposing a scalable strategy in multiple
videos hiding, which can hide variable numbers of secret videos in a cover
video with a single model and a single training session. Extensive experiments
demonstrate that with the significant improvement of the video steganography
performance, our proposed LF-VSN has high security, large hiding capacity, and
flexibility. The source code is available at this https URL

本文介绍了一种大容量和灵活的视频隐写网络 (LF-VSN)，提出了一种可逆的管道，可以通过单个神经网络隐藏 / 恢复多个秘密视频，还介绍了一种可控密钥方案，使不同的接收者能够从相同的覆盖视频中恢复特定的秘密视频。同时，为多个视频隐藏提供了可伸缩策略，可以通过单个模型和单个训练会话在覆盖视频中隐藏不同数量的秘密视频。经过广泛的实验证明，在视频隐写性能显著提高的同时，我们提出的 LF-VSN 具有高安全性，大隐藏容量和灵活性。

基于可逆神经网络的大容量、灵活的视频隐写术

Large-capacity and Flexible Video Steganography via Invertible Neural  Network

Recently, successful applications of reinforcement learning to chip placement
have emerged. Pretrained models are necessary to improve efficiency and
effectiveness. Currently, the weights of objective metrics (e.g., wirelength,
congestion, and timing) are fixed during pretraining. However, fixed-weighed
models cannot generate the diversity of placements required for engineers to
accommodate changing requirements as they arise. This paper proposes flexible
multiple-objective reinforcement learning (MORL) to support objective functions
with inference-time variable weights using just a single pretrained model. Our
macro placement results show that MORL can generate the Pareto frontier of
multiple objectives effectively.

本文提出一种灵活的多目标强化学习方法 (MORL)，使用单个预先训练的模型来支持具有推理时间变量权重的目标函数，并成功地将其应用于芯片布局中，有效地生成多个目标的 Pareto 前沿。