论文标题
通过智能通信渠道协调多个代理商之间的政策
Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel
论文作者
论文摘要
在多机构增强学习（MARL）中，经常引入专门的渠道，这些渠道允许代理人直接互相交流。在本文中，我们提出了一种替代方法，代理商通过智能主持人进行沟通，该智能促进者学会筛选并解释所有代理商提供的信号，以改善代理商的集体绩效。为了确保此促进者不会成为集中式控制器，激励代理以减少对其传达信息的依赖，并且消息只能影响从固定集合的策略选择，而不是给定策略的即时操作。我们证明了这种体系结构对几个合作MARL环境的现有基线的实力。
In Multi-Agent Reinforcement Learning (MARL), specialized channels are often introduced that allow agents to communicate directly with one another. In this paper, we propose an alternative approach whereby agents communicate through an intelligent facilitator that learns to sift through and interpret signals provided by all agents to improve the agents' collective performance. To ensure that this facilitator does not become a centralized controller, agents are incentivized to reduce their dependence on the messages it conveys, and the messages can only influence the selection of a policy from a fixed set, not instantaneous actions given the policy. We demonstrate the strength of this architecture over existing baselines on several cooperative MARL environments.
