Mobile edge computing (MEC) is considered a novel paradigm for
computation-intensive and delay-sensitive tasks in fifth generation (5G)
networks and beyond. However, its uncertainty, referred to as dynamic and
randomness, from the mobile device, wireless channel, and edge network sides,
results in high-dimensional, nonconvex, nonlinear, and NP-hard optimization
problems. Thanks to the evolved reinforcement learning (RL), upon iteratively
interacting with the dynamic and random environment, its trained agent can
intelligently obtain the optimal policy in MEC. Furthermore, its evolved
versions, such as deep RL (DRL), can achieve higher convergence speed
efficiency and learning accuracy based on the parametric approximation for the
large-scale state-action space. This paper provides a comprehensive research
review on RL-enabled MEC and offers insight for development in this area. More
importantly, associated with free mobility, dynamic channels, and distributed
services, the MEC challenges that can be solved by different kinds of RL
algorithms are identified, followed by how they can be solved by RL solutions
in diverse mobile applications. Finally, the open challenges are discussed to
provide helpful guidance for future research in RL training and learning MEC.

本文综述了强化学习 - enabled 移动边缘计算在不同移动应用中的发展，并探讨了其面临的挑战和未来研究方向。

强化学习增强的移动边缘计算用于 6G 边缘智能

Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence

Real-time navigation in dense human environments is a challenging problem in
robotics. Most existing path planners fail to account for the dynamics of
pedestrians because introducing time as an additional dimension in search space
is computationally prohibitive. Alternatively, most local motion planners only
address imminent collision avoidance and fail to offer long-term optimality. In
this work, we present an approach, called Dynamic Channels, to solve this
global to local quandary. Our method combines the high-level topological path
planning with low-level motion planning into a complete pipeline. By
formulating the path planning problem as graph searching in the triangulation
space, our planner is able to explicitly reason about the obstacle dynamics and
capture the environmental change efficiently. We evaluate efficiency and
performance of our approach on public pedestrian datasets and compare it to a
state-of-the-art planning algorithm for dynamic obstacle avoidance.

Dynamic Channels 是一种在机器人密集人类环境中进行实时导航的新方法，能够通过在三角空间中基于图搜索来解决路径规划问题，考虑障碍物动态并高效地捕捉环境变化，具备较高的效率和性能，并可用于动态障碍物避免。

动态通道：人群导航规划框架

Dynamic Channel: A Planning Framework for Crowd Navigation

This paper describes the architecture and performance of ORACLE, an approach
for detecting a unique radio from a large pool of bit-similar devices (same
hardware, protocol, physical address, MAC ID) using only IQ samples at the
physical layer. ORACLE trains a convolutional neural network (CNN) that
balances computational time and accuracy, showing 99\% classification accuracy
for a 16-node USRP X310 SDR testbed and an external database of $>$100 COTS
WiFi devices. Our work makes the following contributions: (i) it studies the
hardware-centric features within the transmitter chain that causes IQ sample
variations; (ii) for an idealized static channel environment, it proposes a CNN
architecture requiring only raw IQ samples accessible at the front-end, without
channel estimation or prior knowledge of the communication protocol; (iii) for
dynamic channels, it demonstrates a principled method of feedback-driven
transmitter-side modifications that uses channel estimation at the receiver to
increase differentiability for the CNN classifier. The key innovation here is
to intentionally introduce controlled imperfections on the transmitter side
through software directives, while minimizing the change in bit error rate.
Unlike previous work that imposes constant environmental conditions, ORACLE
adopts the `train once deploy anywhere' paradigm with near-perfect device
classification accuracy.

ORACLE 是一种检测来自大量相似设备的独特无线电信号的方法，它利用卷积神经网络（CNN），能够平衡计算时间和准确性，而且只需物理层的 IQ 样本来训练。它通过软件指令有意引入控制性的不完美之处，以提高 CNN 的分类器的可区分度，而且呈现出 99％的分类精度。