Social dilemmas can be considered situations where individual rationality leads to collective irrationality. The multi-agent reinforcement learning community has leveraged ideas from social science, such as social value orientations (SVO), to solve social dilemmas in complex cooperative tasks. In this paper, by first introducing the typical "division of labor or roles" mechanism in human society, we provide a promising solution for intertemporal social dilemmas (ISD) with SVOs. A novel learning framework, called Learning Roles with Emergent SVOs (RESVO), is proposed to transform the learning of roles into the social value orientation emergence, which is symmetrically solved by endowing agents with altruism to share rewards with other agents. An SVO-based role embedding space is then constructed by individual conditioning policies on roles with a novel rank regularizer and mutual information maximizer. Experiments show that RESVO achieves a stable division of labor and cooperation in ISDs with different complexity.

本文提出了一种名为Learning Roles with Emergent SVOs (RESVO)的学习框架，结合社会科学中的社会价值取向（SVO）解决共同合作任务中的社会困境问题，通过对角色分配和奖励共享机制的学习，构建了一个基于SVO的角色嵌入空间，通过实验验证了该框架能够有效地解决不同复杂度的ISD问题。

通过新兴的社会价值观学习角色