We consider a cooperative learning scenario where a collection of networked
agents with individually owned classifiers dynamically update their
predictions, for the same classification task, through communication or
observations of each other's predictions. Clearly if highly influential
vertices use erroneous classifiers, there will be a negative effect on the
accuracy of all the agents in the network. We ask the following question: how
can we optimally fix the prediction of a few classifiers so as maximize the
overall accuracy in the entire network. To this end we consider an aggregate
and an egalitarian objective function. We show a polynomial time algorithm for
optimizing the aggregate objective function, and show that optimizing the
egalitarian objective function is NP-hard. Furthermore, we develop
approximation algorithms for the egalitarian improvement. The performance of
all of our algorithms are guaranteed by mathematical analysis and backed by
experiments on synthetic and real data.

协作学习中，网络代理以自己拥有的分类器通过交流或观察其他代理的预测进行动态更新，研究了在此情景中如何优化预测以提高整个网络的准确性，并提出了多项式时间算法和近似算法来实现优化的目标。

最佳优化社交环境中的合作学习

Optimally Improving Cooperative Learning in a Social Setting

Multi-agent reinforcement learning (MARL) has long been a significant and
everlasting research topic in both machine learning and control. With the
recent development of (single-agent) deep RL, there is a resurgence of
interests in developing new MARL algorithms, especially those that are backed
by theoretical analysis. In this paper, we review some recent advances a
sub-area of this topic: decentralized MARL with networked agents. Specifically,
multiple agents perform sequential decision-making in a common environment,
without the coordination of any central controller. Instead, the agents are
allowed to exchange information with their neighbors over a communication
network. Such a setting finds broad applications in the control and operation
of robots, unmanned vehicles, mobile sensor networks, and smart grid. This
review is built upon several our research endeavors in this direction, together
with some progresses made by other researchers along the line. We hope this
review to inspire the devotion of more research efforts to this exciting yet
challenging area.

本文回顾了多智能体强化学习的一个分支领域 —— 网络化智能体下的去中心化多智能体强化学习。该领域的研究主要集中在多个代理在公共环境中执行顺序决策，而无需任何中央控制器的协调。代理可以通过通信网络与其邻居交换信息。此设置在机器人，无人驾驶车辆，移动传感器网络和智能电网的控制和操作中具有广泛应用。

网络代理的去中心化多智能体强化学习：最新进展

Decentralized Multi-Agent Reinforcement Learning with Networked Agents:  Recent Advances

Despite the increasing interest in multi-agent reinforcement learning (MARL)
in multiple communities, understanding its theoretical foundation has long been
recognized as a challenging problem. In this work, we address this problem by
providing a finite-sample analysis for decentralized batch MARL with networked
agents. Specifically, we consider two decentralized MARL settings, where teams
of agents are connected by time-varying communication networks, and either
collaborate or compete in a zero-sum game setting, without any central
controller. These settings cover many conventional MARL settings in the
literature. For both settings, we develop batch MARL algorithms that can be
implemented in a decentralized fashion, and quantify the finite-sample errors
of the estimated action-value functions. Our error analysis captures how the
function class, the number of samples within each iteration, and the number of
iterations determine the statistical accuracy of the proposed algorithms. Our
results, compared to the finite-sample bounds for single-agent RL, involve
additional error terms caused by decentralized computation, which is inherent
in our decentralized MARL setting. This work appears to be the first
finite-sample analysis for batch MARL, a step towards rigorous theoretical
understanding of general MARL algorithms in the finite-sample regime.

本文提供了分布式 batch MARL 的有限样本分析，比较传统单智能体强化学习的有限样本错误较大，由于分布式计算造成的，这是对一般 MARL 算法在有限样本范围内严格的理论理解的一步。