论文标题
多代理系统中的算法：增强学习和游戏理论的整体观点
Algorithms in Multi-Agent Systems: A Holistic Perspective from Reinforcement Learning and Game Theory
论文作者
论文摘要
近年来，深入的增强学习（RL）取得了出色的成果，这导致了方法和应用的数量急剧增加。最近的作品正在探索超出单格场景的学习，并考虑多代理的方案。但是，他们面临许多挑战，并正在寻求传统游戏理论算法的帮助，而传统游戏理论算法又显示出明亮的应用承诺与现代算法相结合并增强计算能力。在这项调查中，我们首先介绍了单个代理RL和多代理系统中的基本概念和算法。然后，我们从三个方面总结了相关算法。游戏理论的解决方案概念为试图评估代理或在多代理系统中找到更好的解决方案的算法提供了灵感。虚拟的自我戏剧变得流行，并对多机构增强学习的算法产生了重大影响。反事实遗憾的最小化是用不完整的信息来解决游戏的重要工具，并在深入学习结合使用时表现出很大的力量。
Deep reinforcement learning (RL) has achieved outstanding results in recent years, which has led a dramatic increase in the number of methods and applications. Recent works are exploring learning beyond single-agent scenarios and considering multi-agent scenarios. However, they are faced with lots of challenges and are seeking for help from traditional game-theoretic algorithms, which, in turn, show bright application promise combined with modern algorithms and boosting computing power. In this survey, we first introduce basic concepts and algorithms in single agent RL and multi-agent systems; then, we summarize the related algorithms from three aspects. Solution concepts from game theory give inspiration to algorithms which try to evaluate the agents or find better solutions in multi-agent systems. Fictitious self-play becomes popular and has a great impact on the algorithm of multi-agent reinforcement learning. Counterfactual regret minimization is an important tool to solve games with incomplete information, and has shown great strength when combined with deep learning.
