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Social dilemmas can be considered situations where individual rationalityleads to collective irrationality. The multi-agent reinforcement learningcommunity has leveraged ideas from social science, such as social valueorientations (SVO), to solve social dilemmas in complex cooperative tasks. Inthis paper, by first introducing the typical "division of labor or roles"mechanism in human society, we provide a promising solution for intertemporalsocial dilemmas (ISD) with SVOs. A novel learning framework, called LearningRoles with Emergent SVOs (RESVO), is proposed to transform the learning ofroles into the social value orientation emergence, which is symmetricallysolved by endowing agents with altruism to share rewards with other agents. AnSVO-based role embedding space is then constructed by individual conditioningpolicies on roles with a novel rank regularizer and mutual informationmaximizer. Experiments show that RESVO achieves a stable division of labor andcooperation in ISDs with different complexity.

Learning Roles with Emergent Social Value Orientations