RI: Small: Understanding Value-based Multiagent Learning and Its Applications

这个项目探讨了多智能体环境中基于价值的学习方法的行为。基于价值的方法通过使用经验来估计备选方案的效用影响并选择具有高预测价值的方案来做出决策。因为它们评估行为的组成部分，而不是将行为视为原子单元，所以它们在计算和统计上都是有效的。虽然这些方法已经在计算实验中使用了很多年，但直到最近研究人员才开始正式描述它们的行为。我们自己的初步工作是发现，一些基于价值的方法表现出超级纳什行为，使他们特别值得研究。更具体地说，我们正在分析，数学和实验，如何基于价值的算法在几类不同复杂性的模拟游戏从人工智能社区，多智能体工程应用程序从无线网络领域绘制，并与认知神经科学家合作，作为人类和动物决策的模型。在可能的情况下，我们正在改进现有的基于值的算法，使其比现有算法更有效，更强大，更通用。我们还在设计教育推广活动，包括制作关于如何在现实生活中的社会困境中促进合作行为的娱乐性教学视频。

This project explores the behavior of value-based learning methods in multi-agent environments. Value-based methods make decisions by using experience to estimate the utility impact of alternatives and choosing those with high predicted value. Because they evaluate components of behavior instead of treating behaviors as atomic units, they are computationally and statistically efficient. While these methods have been used in computational experiments for many years, only recently have researchers begun to formally characterize their behavior. Our own preliminary work is finding that some value-based methods exhibit super-Nash behavior, making them particularly worthy of study.More specifically, we are analyzing, mathematically and experimentally, how value-based algorithms perform in several classes of simulated games of varying complexity from the artificial intelligence community, multi-agent engineering applications drawn from the wireless networking area, and as models of human and animal decision making in collaboration with cognitive neuroscientists. Where possible, we are refining existing value-based algorithms to work more efficiently, robustly, and generally than existing algorithms. We are also designing educational outreach activities, including creating entertaining instructional videos on how to promote cooperative behavior in real-life social dilemmas.