异构图注意网络在学习多样性交流中的应用
Heterogeneous Graph Attention Networks for Learning Diverse Communication
摘要：
当参与的代理之间存在通信时，多代理团队可以实现更好的性能，从而允许他们协调他们的行动以最大化共享效用。然而，当一组代理与不同的行动和观察空间合作时，信息共享并不简单，需要定制的通信协议，这取决于发送者和接收者类型。如果没有适当的建模，这种异质性的代理，通信变得不那么有用，甚至可能恶化的多代理合作的性能。我们提出了异构图注意力网络，称为HetNet，学习高效和多样化的通信模型，协调异构代理完成任务的协作性质。我们提出了一个多智能体异构演员批评（MAHAC）的学习范式，以获得合作的每类政策和有效的通信协议的复合机器人团队。我们提出的框架进行评估，对多个基线在一个复杂的环境中，不同类型的代理人必须进行沟通和合作，以满足目标。实验结果表明，HetNet在学习复杂的多智能体通信协议方面优于基线，性能指标提高了$\sim$ 10\%.
英文摘要：
Multi-agent teaming achieves better performance when there is communication among participating agents allowing them to coordinate their actions for maximizing shared utility. However, when collaborating a team of agents with different action and observation spaces, information sharing is not straightforward and requires customized communication protocols, depending on sender and receiver types. Without properly modeling such heterogeneity in agents, communication becomes less helpful and could even deteriorate the multi-agent cooperation performance. We propose heterogeneous graph attention networks, called HetNet, to learn efficient and diverse communication models for coordinating heterogeneous agents towards accomplishing tasks that are of collaborative nature. We propose a Multi-Agent Heterogeneous Actor-Critic (MAHAC) learning paradigm to obtain collaborative per-class policies and effective communication protocols for composite robot teams. Our proposed framework is evaluated against multiple baselines in a complex environment in which agents of different types must communicate and cooperate to satisfy the objectives. Experimental results show that HetNet outperforms the baselines in learning sophisticated multi-agent communication protocols by achieving $\sim$10\% improvements in performance metrics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
