Multi-agent reinforcement learning (MARL) has emerged as a useful approach to
solving decentralised decision-making problems at scale. Research in the field
has been growing steadily with many breakthrough algorithms proposed in recent
years. In this work, we take a closer look at this rapid development with a
focus on evaluation methodologies employed across a large body of research in
cooperative MARL. By conducting a detailed meta-analysis of prior work,
spanning 75 papers accepted for publication from 2016 to 2022, we bring to
light worrying trends that put into question the true rate of progress. We
further consider these trends in a wider context and take inspiration from
single-agent RL literature on similar issues with recommendations that remain
applicable to MARL. Combining these recommendations, with novel insights from
our analysis, we propose a standardised performance evaluation protocol for
cooperative MARL. We argue that such a standard protocol, if widely adopted,
would greatly improve the validity and credibility of future research, make
replication and reproducibility easier, as well as improve the ability of the
field to accurately gauge the rate of progress over time by being able to make
sound comparisons across different works. Finally, we release our meta-analysis
data publicly on our project website for future research on evaluation:
this https URL

本篇论文通过元分析探究了近年来多智能体强化学习中合作场景下的评价方法，并提出了一个标准化的性能评估协议以提高未来相关研究的可信度与可复现性。