We study the problem of distributed and rate-adaptive feature compression for
linear regression. A set of distributed sensors collect disjoint features of
regressor data. A fusion center is assumed to contain a pretrained linear
regression model, trained on a dataset of the entire uncompressed data. At
inference time, the sensors compress their observations and send them to the
fusion center through communication-constrained channels, whose rates can
change with time. Our goal is to design a feature compression {scheme} that can
adapt to the varying communication constraints, while maximizing the inference
performance at the fusion center. We first obtain the form of optimal
quantizers assuming knowledge of underlying regressor data distribution. Under
a practically reasonable approximation, we then propose a distributed
compression scheme which works by quantizing a one-dimensional projection of
the sensor data. We also propose a simple adaptive scheme for handling changes
in communication constraints. We demonstrate the effectiveness of the
distributed adaptive compression scheme through simulated experiments.

我们研究了分布式和自适应速率的特征压缩问题，通过传感器收集线性回归模型的不同特征，设计了一种特征压缩方案以适应不断变化的通信约束，并通过模拟实验证明了该方案的有效性。

分布式和自适应速率特征压缩

Distributed and Rate-Adaptive Feature Compression

The offline reinforcement learning (RL) paradigm provides a general recipe to
convert static behavior datasets into policies that can perform better than the
policy that collected the data. While policy constraints, conservatism, and
other methods for mitigating distributional shifts have made offline
reinforcement learning more effective, the continuous action setting often
necessitates various approximations for applying these techniques. Many of
these challenges are greatly alleviated in discrete action settings, where
offline RL constraints and regularizers can often be computed more precisely or
even exactly. In this paper, we propose an adaptive scheme for action
quantization. We use a VQ-VAE to learn state-conditioned action quantization,
avoiding the exponential blowup that comes with na\"ive discretization of the
action space. We show that several state-of-the-art offline RL methods such as
IQL, CQL, and BRAC improve in performance on benchmarks when combined with our
proposed discretization scheme. We further validate our approach on a set of
challenging long-horizon complex robotic manipulation tasks in the Robomimic
environment, where our discretized offline RL algorithms are able to improve
upon their continuous counterparts by 2-3x. Our project page is at
this https URL

我们提出了一种自适应的行动量化方案，通过使用 VQ-VAE 学习状态条件的行动量化，避免了行动空间的指数爆炸问题，并通过离线强化学习方法在基准测试中改进了性能，同时在 Robomimic 环境中的复杂机器人操作任务中，离线强化学习算法通过离散化相对于连续方法实现了 2-3 倍的改进。

机器人技能学习的动作量化离线强化学习

Action-Quantized Offline Reinforcement Learning for Robotic Skill  Learning

We propose a new Monte Carlo method for sampling from multimodal
distributions. The idea of this technique is based on splitting the task into
two: finding the modes of a target distribution $\pi$ and sampling, given the
knowledge of the locations of the modes. The sampling algorithm relies on steps
of two types: local ones, preserving the mode; and jumps to regions associated
with different modes. Besides, the method learns the optimal parameters of the
algorithm while it runs, without requiring user intervention. Our technique
should be considered as a flexible framework, in which the design of moves can
follow various strategies known from the broad MCMC literature.
In order to design an adaptive scheme that facilitates both local and jump
moves, we introduce an auxiliary variable representing each mode and we define
a new target distribution $\tilde{\pi}$ on an augmented state space
$\mathcal{X}~\times~\mathcal{I}$, where $\mathcal{X}$ is the original state
space of $\pi$ and $\mathcal{I}$ is the set of the modes. As the algorithm runs
and updates its parameters, the target distribution $\tilde{\pi}$ also keeps
being modified. This motivates a new class of algorithms, Auxiliary Variable
Adaptive MCMC. We prove general ergodic results for the whole class before
specialising to the case of our algorithm.

提出了一种基于蒙特卡罗方法的采样技术，使用辅助变量设计了自适应方案来实现多模态分布下的本地移动和跳跃移动，并证明了算法的遍历性。