Swarm robotics is envisioned to automate a large number of dirty, dangerous,
and dull tasks. Robots have limited energy, computation capability, and
communication resources. Therefore, current swarm robotics have a small number
of robots, which can only provide limited spatio-temporal information. In this
paper, we propose to leverage the mobile edge computing to alleviate the
computation burden. We develop an effective solution based on a mobility-aware
deep reinforcement learning model at the edge server side for computing
scheduling and resource. Our results show that the proposed approach can meet
delay requirements and guarantee computation precision by using minimum robot
energy.

通过利用移动边缘计算来减轻计算负担，本文提出了一种基于在边缘服务器端的可移动感知深度强化学习模型的有效解决方案，用于计算调度和资源，结果表明该方法可以通过使用最少的机器人能量满足延迟要求并保证计算精度。

基于深度强化学习的群体机器人移动感知计算卸载

Mobility-Aware Computation Offloading for Swarm Robotics using Deep  Reinforcement Learning

We study scheduling of computation tasks across n workers in a large scale
distributed learning problem with the help of a master. Computation and
communication delays are assumed to be random, and redundant computations are
assigned to workers in order to tolerate stragglers. We consider sequential
computation of tasks assigned to a worker, while the result of each computation
is sent to the master right after its completion. Each computation round, which
can model an iteration of the stochastic gradient descent (SGD) algorithm, is
completed once the master receives k distinct computations, referred to as the
computation target. Our goal is to characterize the average completion time as
a function of the computation load, which denotes the portion of the dataset
available at each worker, and the computation target. We propose two
computation scheduling schemes that specify the tasks assigned to each worker,
as well as their computation schedule, i.e., the order of execution. Assuming a
general statistical model for computation and communication delays, we derive
the average completion time of the proposed schemes. We also establish a lower
bound on the minimum average completion time by assuming prior knowledge of the
random delays. Experimental results carried out on Amazon EC2 cluster show a
significant reduction in the average completion time over existing coded and
uncoded computing schemes. It is also shown numerically that the gap between
the proposed scheme and the lower bound is relatively small, confirming the
efficiency of the proposed scheduling design.

本文研究分布式学习中多个工作者的计算任务调度问题，提出了两种计算调度方案，以及基于计算和通信延迟的平均完成时间分析，实验证明所提出的方案有效且接近最优解。