Modern Reinforcement Learning (RL) algorithms are able to outperform humans in a wide variety of tasks. Multi-agent reinforcement learning (MARL) settings present additional challenges, and successful cooperation in mixed-motive groups of agents depends on a delicate balancing act between individual and group objectives. Social conventions and norms, often inspired by human institutions, are used as tools for striking this balance. In this paper, we examine a fundamental, well-studied social convention that underlies cooperation in both animal and human societies: dominance hierarchies. We adapt the ethological theory of dominance hierarchies to artificial agents, borrowing the established terminology and definitions with as few amendments as possible. We demonstrate that populations of RL agents, operating without explicit programming or intrinsic rewards, can invent, learn, enforce, and transmit a dominance hierarchy to new populations. The dominance hierarchies that emerge have a similar structure to those studied in chickens, mice, fish, and other species.

翻译：现代强化学习（RL）算法能够在多种任务中超越人类表现。多智能体强化学习（MARL）场景带来了额外挑战，混合动机智能体群体中的成功合作依赖于个体目标与群体目标之间的微妙平衡。受人类社会制度启发的社会惯例与规范常被用作实现这种平衡的工具。本文研究一个基础且经过充分研究的社会惯例——优势等级制度，该制度支撑着动物与人类社会中的合作行为。我们将优势等级制度的动物行为学理论适配至人工智能体，在尽可能少修改的前提下沿用现有术语与定义。研究表明，未经过显式编程或内在奖励训练的RL智能体群体，能够自发创造、学习、强化并传播优势等级制度至新群体。这些涌现的优势等级结构与在鸡、鼠、鱼及其他物种中观察到的结构具有相似性。