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.

中文翻译：

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多智能体系统中的算法：强化学习和博弈论的整体视角

深度强化学习（RL）近年来取得了突出的成果，导致方法和应用的数量急剧增加。最近的工作正在探索超越单代理场景的学习并考虑多代理场景。然而，他们面临着许多挑战，正在寻求传统博弈论算法的帮助，而传统博弈论算法又显示出与现代算法相结合并提高计算能力的光明应用前景。在本次调查中，我们首先介绍了单代理 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的算法。博弈论中的解决方案概念为尝试评估代理或在多代理系统中找到更好解决方案的算法提供了灵感。虚构的自我对弈变得流行，对多智能体强化学习的算法产生了很大的影响。反事实后悔最小化是解决信息不完整博弈的重要工具，与深度学习结合时显示出巨大的优势。