Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has led to a dramatic increase in the number of applications and methods. Recent works have explored learning beyond single-agent scenarios and have considered multiagent learning (MAL) scenarios. Initial results report successes in complex multiagent domains, although there are several challenges to be addressed. The primary goal of this article is to provide a clear overview of current multiagent deep reinforcement learning (MDRL) literature. Additionally, we complement the overview with a broader analysis: (i) we revisit previous key components, originally presented in MAL and RL, and highlight how they have been adapted to multiagent deep reinforcement learning settings. (ii) We provide general guidelines to new practitioners in the area: describing lessons learned from MDRL works, pointing to recent benchmarks, and outlining open avenues of research. (iii) We take a more critical tone raising practical challenges of MDRL (e.g., implementation and computational demands). We expect this article will help unify and motivate future research to take advantage of the abundant literature that exists (e.g., RL and MAL) in a joint effort to promote fruitful research in the multiagent community.

中文翻译：

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多主体深度强化学习的调查与批判

近年来，深度强化学习（RL）取得了出色的成绩。这导致应用程序和方法的数量急剧增加。最近的工作已经探索了超出单代理场景的学习，并考虑了多代理学习（MAL）场景。初步结果报告了在复杂的多代理域中的成功，尽管有许多挑战需要解决。本文的主要目的是为当前的多主体深度强化学习（MDRL）文献提供清晰的概述。此外，我们通过更广泛的分析来补充概述：（i）我们回顾以前在MAL和RL中介绍的以前的关键组件，并强调它们如何适应多主体深度强化学习设置。（ii）我们向该领域的新开业者提供一般指导：描述从MDRL工作中学到的经验教训，指出最新的基准，并概述开放的研究途径。（iii）我们采取了更为批判的态度，提出了MDRL的实际挑战（例如，实施和计算需求）。我们希望本文将有助于统一和激发未来的研究，以利用现有的大量文献（例如RL和MAL），共同努力在多主体社区中促进富有成果的研究。