Solving the problem of cooperation is of fundamental importance to the creation and maintenance of functional societies, with examples of cooperative dilemmas ranging from navigating busy road junctions to negotiating carbon reduction treaties. As the use of AI becomes more pervasive throughout society, the need for socially intelligent agents that are able to navigate these complex cooperative dilemmas is becoming increasingly evident. In the natural world, direct punishment is an ubiquitous social mechanism that has been shown to benefit the emergence of cooperation within populations. However no prior work has investigated its impact on the development of cooperation within populations of artificial learning agents experiencing social dilemmas. Additionally, within natural populations the use of any form of punishment is strongly coupled with the related social mechanisms of partner selection and reputation. However, no previous work has considered the impact of combining multiple social mechanisms on the emergence of cooperation in multi-agent systems. Therefore, in this paper we present a comprehensive analysis of the behaviours and learning dynamics associated with direct punishment in multi-agent reinforcement learning systems and how it compares to third-party punishment, when both are combined with the related social mechanisms of partner selection and reputation. We provide an extensive and systematic evaluation of the impact of these key mechanisms on the dynamics of the strategies learned by agents. Finally, we discuss the implications of the use of these mechanisms on the design of cooperative AI systems.

翻译：解决合作问题对于创建和维持功能性社会至关重要，合作困境的例子从应对繁忙的路口到协商碳减排条约等。随着人工智能在社会中的广泛应用，能够应对这些复杂合作困境的社会智能体需求日益凸显。在自然界中，直接惩罚是一种普遍存在的社会机制，已被证明有利于群体中合作的涌现。然而，先前的研究尚未探讨其对经历社会困境的人工学习智能体群体中合作发展的影响。此外，在自然群体中，任何形式的惩罚都与伙伴选择和声誉等相关社会机制紧密耦合。然而，尚无先前研究考虑将多种社会机制结合对多智能体系统中合作涌现的影响。因此，本文全面分析了多智能体强化学习系统中与直接惩罚相关的行为和学习动态，并将其与第三方惩罚在结合伙伴选择和声誉等社会机制时进行比较。我们系统地评估了这些关键机制对智能体学习策略动态的广泛影响。最后，我们讨论了这些机制在合作型人工智能系统设计中的启示。