We examine the problem of transmission control, i.e., when to transmit, in
distributed wireless communications networks through the lens of multi-agent
reinforcement learning. Most other works using reinforcement learning to
control or schedule transmissions use some centralized control mechanism,
whereas our approach is fully distributed. Each transmitter node is an
independent reinforcement learning agent and does not have direct knowledge of
the actions taken by other agents. We consider the case where only a subset of
agents can successfully transmit at a time, so each agent must learn to act
cooperatively with other agents. An agent may decide to transmit a certain
number of steps into the future, but this decision is not communicated to the
other agents, so it the task of the individual agents to attempt to transmit at
appropriate times. We achieve this collaborative behavior through studying the
effects of different actions spaces. We are agnostic to the physical layer,
which makes our approach applicable to many types of networks. We submit that
approaches similar to ours may be useful in other domains that use multi-agent
reinforcement learning with independent agents.

该研究以多智能体强化学习的角度研究了分布式无线通信网络中的传输控制问题，其方法是基于完全分布式的独立强化学习代理实现的协同行为，其中每个智能体节点只对自己的行为决策进行学习，以实现在不同的行动空间下的协同行为。

利用多智能体强化学习的分布式无线网络传输控制

Distributed Transmission Control for Wireless Networks using Multi-Agent  Reinforcement Learning

In the past years, power grids have become a valuable target for
cyber-attacks. Especially the attacks on the Ukrainian power grid has sparked
numerous research into possible attack vectors, their extent, and possible
mitigations. However, many fail to consider realistic scenarios in which time
series are incorporated into simulations to reflect the transient behaviour of
independent generators and consumers. Moreover, very few consider the limited
sensory input of a potential attacker. In this paper, we describe a reactive
power attack based on a well-understood scenario. We show that independent
agents can learn to use the dynamics of the power grid against it and that the
attack works even in the face of other generator and consumer nodes acting
independently.

本文介绍了一种基于动态学习的无功功率攻击方法，该攻击可以针对独立工作的电力系统，并且可以在其他发电机和使用者独立操作的情况下有效。