In this paper, we apply an multi-agent reinforcement learning (MARL)
framework allowing the base station (BS) and the user equipments (UEs) to
jointly learn a channel access policy and its signaling in a wireless multiple
access scenario. In this framework, the BS and UEs are reinforcement learning
(RL) agents that need to cooperate in order to deliver data. The comparison
with a contention-free and a contention-based baselines shows that our
framework achieves a superior performance in terms of goodput even in high
traffic situations while maintaining a low collision rate. The scalability of
the proposed method is studied, since it is a major problem in MARL and this
paper provides the first results in order to address it.

本文利用多智能体强化学习框架，让基站和用户设备一起学习在无线多点接入场景中的信道访问策略和信令，比较竞争自由和基于竞争的基线，结果显示我们的框架在高流量情况下保持低冲突率的同时，在吞吐量方面表现出色。同时本文研究了所提出方法的可扩展性，并为解决该问题提供了首个结果。

可扩展的联合学习无线多址策略及其信令

Scalable Joint Learning of Wireless Multiple-Access Policies and their Signaling

Signaling is an important topic in the study of asymmetric information in
economic settings. In particular, the transparency of information available to
a seller in an auction setting is a question of major interest. We introduce
the study of signaling when conducting a second price auction of a
probabilistic good whose actual instantiation is known to the auctioneer but
not to the bidders. This framework can be used to model impressions selling in
display advertising. We study the problem of computing a signaling scheme that
maximizes the auctioneer's revenue in a Bayesian setting. While the general
case is proved to be computationally hard, several cases of interest are shown
to be polynomially solvable. In addition, we establish a tight bound on the
minimum number of signals required to implement an optimal signaling scheme and
show that at least half of the maximum social welfare can be preserved within
such a scheme.

本研究探讨在群拍卖中的信息透明度问题，以及计算在贝叶斯状态下最大化拍卖者利润的信号方案。其中，多种情况均被证明可在多项式时间内解决，并建立了实现最优信号方案所需的最小信号数量的紧密界限。同时，研究表明，在该方案中最大的社会福利至少可以实现总和的一半。