论文来源: Multi-Agent Graph-Attention Communication and Teaming (ifaamas.org)多智能体图注意力通信 解决的问题:利用共享信息来降低沟通开销的成本MARL 中的大多数先前工作未能捕捉到智能体之间的复杂关系,…
We propose a novel multi-agent reinforcement learning algorithm, Multi-Agent Graph-attentIon Communication (MAGIC), with a graph-attention communication protocol in which we learn 1) a Scheduler to help with the problems of when to communicate and whom to address messages to, and 2) a Message ...
引入图神经网络:为了解决多智能体空战的实际应用问题,本文引入了图神经网络来提取智能体之间的通信关系,并提出了一种基于软硬两阶段图注意力机制的多智能体通信方法。 实验验证:在模拟空战环境中进行综合实验,结果表明该方法能有效表示智能体之间的交互关系,从而在模拟空战场景中显著提高UAV的学习性能。 3.1 基于图注意...
Multi-agent systemCommunicationGraph attentionAir-to-air combat system is a complex multi-agent system (MAS) wherein a large number of unmanned combat aerial vehicles learn to combat with their opponents in a highly dynamic and uncertain environment. Because of the local observability of each ...
Communication is a critical factor for the big multi-agent world to stay organized and productive. Recently, Deep Reinforcement Learning (DRL) has been adopted to learn the communication among multiple intelligent agents. However, in terms of the DRL setting, the increasing number of communication ...
Experiments in multi-agent particle environments demonstrate that IACN significantly outperforms existing methods in terms of overall performance, coordination effectiveness, and adaptability. 展开 关键词: Multi-agent systems Integrated adaptive communication network MAPPO Graph attention mechanism ...
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We implemented and empirically examined a model where the agents simply try to model each edge in the graph as being either slow, medium, or fast due to the other agents using that edge. Communication on a fixed social network occurs only when an agent changes the speed category it has ...
3.2. Communication Groups Construction 完全连接与群组通信。全连接的通信会导致大量的带宽使用,并且以 O(N2) 的量级增长,其中 N 代表网络中agent的数量。组通信能够修剪不相关的连接,并可以大大降低整体网络的复杂性。 如图2 所示,以前关于学习communicate的工作应用了全连接通信,用于agent之间的信息交换。 为了降低...
这篇题为"Deep Hierarchical Communication Graph in Multi-Agent Reinforcement Learning"(多智能体强化学习中的深层次层次通信图)的论文讨论了一种名为Deep Hierarchical Communication Graph(DHCG)的多智能体强化学习(MARL)的新方法。该论文旨在通过引入一个动态的、依赖于消息的有向无环图(DAG)来模拟智能体之间的依...