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Solving the problem of cooperation is of fundamental importance to thecreation and maintenance of functional societies, with examples of cooperativedilemmas ranging from navigating busy road junctions to negotiating carbonreduction treaties. As the use of AI becomes more pervasive throughout society,the need for socially intelligent agents that are able to navigate thesecomplex cooperative dilemmas is becoming increasingly evident. In the naturalworld, direct punishment is an ubiquitous social mechanism that has been shownto benefit the emergence of cooperation within populations. However no priorwork has investigated its impact on the development of cooperation withinpopulations of artificial learning agents experiencing social dilemmas.Additionally, within natural populations the use of any form of punishment isstrongly coupled with the related social mechanisms of partner selection andreputation. However, no previous work has considered the impact of combiningmultiple social mechanisms on the emergence of cooperation in multi-agentsystems. Therefore, in this paper we present a comprehensive analysis of thebehaviours and learning dynamics associated with direct punishment inmulti-agent reinforcement learning systems and how it compares to third-partypunishment, when both are combined with the related social mechanisms ofpartner selection and reputation. We provide an extensive and systematicevaluation of the impact of these key mechanisms on the dynamics of thestrategies learned by agents. Finally, we discuss the implications of the useof these mechanisms on the design of cooperative AI systems.

Investigating the Impact of Direct Punishment on the Emergence of Cooperation in Multi-Agent Reinforcement Learning Systems