论文标题
一个基于双层复发的分散沟通框架，用于多代理增强学习
A Decentralized Communication Framework based on Dual-Level Recurrence for Multi-Agent Reinforcement Learning
论文作者
论文摘要
我们提出了一个模型，使分散的多个代理以公平和适应性的方式分享他们对环境的看法。在我们的模型中，当前信息和历史观察都被考虑在内，并且它们以相同的经常性模型进行处理，但以不同的形式处理。我们为多代理系统提供了双级复发框架，其中第一个复发发生在通信顺序中，并用于在代理之间传输通信数据，而第二个复发是基于时间顺序的，并结合了每个代理的历史观察结果。开发的通信流将通信信息与记忆分开，但使代理可以通过双重级别的复发分享其历史观察。该设计使代理适应可变的通信对象，而通信结果对这些代理人来说是公平的。我们在部分可观察和完全可观察到的环境中就我们的方法提供了充分的讨论。几个实验的结果表明，我们的方法的表现优于现有的分散通信框架和相应的集中式培训方法。
We propose a model enabling decentralized multiple agents to share their perception of environment in a fair and adaptive way. In our model, both the current message and historical observation are taken into account, and they are handled in the same recurrent model but in different forms. We present a dual-level recurrent communication framework for multi-agent systems, in which the first recurrence occurs in the communication sequence and is used to transmit communication data among agents, while the second recurrence is based on the time sequence and combines the historical observations for each agent. The developed communication flow separates communication messages from memories but allows agents to share their historical observations by the dual-level recurrence. This design makes agents adapt to changeable communication objects, while the communication results are fair to these agents. We provide a sufficient discussion about our method in both partially observable and fully observable environments. The results of several experiments suggest our method outperforms the existing decentralized communication frameworks and the corresponding centralized training method.
