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NVIF:邻近变异信息流用于合作性大规模多代理增强学习
IEEE transactions on neural networks and learning systems
|September 6, 2023
概括
邻近变异信息流 (NVIF) 通过优化代理之间信息交换来增强多代理强化学习 (MARL) 中的通信. 这种方法稳定了培训,并改善了大规模系统中的合作.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 基于沟通的多代理强化学习 (MARL) 显示了代理合作的前景.
- 现有的MARL方法在大型系统中扎着信息冗余和不稳定的训练.
研究的目的:
- 引入邻近变量信息流 (NVIF) 以提高马尔语的沟通.
- 解决大型和复杂的多代理系统中现有方法的局限性.
主要方法:
- NVIF通过向代理人提供最大信息集 (MIS) 来增强通信.
- 两阶段的培训机制稳定了这一过程:NVIF的线下预培训,随后是在线政策培训.
- 使用邻近通信,NVIF将信息压缩成一个紧的隐藏状态.
主要成果:
- NVIF-近距离政策优化 (PPO) 显示了促进与代理特定奖励合作的潜力.
- 实验结果表明,NVIF在异质和同质的多剂环境中都具有优势.
- 该方法显示了多任务学习应用程序的潜力.
结论:
- 基于通信的MARL,NVIF提供了一种稳定有效的方法.
- 拟议的方法可以在大型多代理系统中改善合作和性能.
- 在MARL中,NVIF是一种有前途的多任务学习技术.
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