Learning Practical Communication Strategies in Cooperative Multi-Agent Reinforcement Learning

Diyi Hu, Chi Zhang, V. Prasanna, Bhaskar, Krishnamachari
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Abstract

In Multi-Agent Reinforcement Learning, communication is critical to encourage cooperation among agents. Communication in realistic wireless networks can be highly unreliable due to network conditions varying with agents' mobility, and stochasticity in the transmission process. We propose a framework to learn practical communication strategies by addressing three fundamental questions: (1) When: Agents learn the timing of communication based on not only message importance but also wireless channel conditions. (2) What: Agents augment message contents with wireless network measurements to better select the game and communication actions. (3) How: Agents use a novel neural message encoder to preserve all information from received messages, regardless of the number and order of messages. Simulating standard benchmarks under realistic wireless network settings, we show significant improvements in game performance, convergence speed and communication efficiency compared with state-of-the-art.
协同多智能体强化学习中的实用沟通策略学习
在多智能体强化学习中,沟通是促进智能体之间合作的关键。在现实无线网络中,由于网络条件随智能体的移动性和传输过程的随机性而变化,通信存在高度不可靠的问题。我们提出了一个框架,通过解决三个基本问题来学习实际的通信策略:(1)何时:智能体不仅根据消息的重要性,而且根据无线信道条件学习通信的时机。(2) What: agent通过无线网络测量来增强消息内容,以更好地选择游戏和通信动作。(3)方法:智能体使用一种新的神经信息编码器来保存接收到的信息中的所有信息,而不考虑消息的数量和顺序。在真实的无线网络设置下模拟标准基准,我们展示了与最先进的游戏性能,收敛速度和通信效率相比的显着改进。
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