How have individuals of social animals in nature evolved to learn from each other, and what would be the optimal strategy for such learning in a specific environment? Here, we address both problems by employing a deep reinforcement learning model to optimize the social learning strategies (SLSs) of agents in a cooperative game in a multi-dimensional landscape. Throughout the training for maximizing the overall payoff, we find that the agent spontaneously learns various concepts of social learning, such as copying, focusing on frequent and well-performing neighbors, self-comparison, and the importance of balancing between individual and social learning, without any explicit guidance or prior knowledge about the system. The SLS from a fully trained agent outperforms all of the traditional, baseline SLSs in terms of mean payoff. We demonstrate the superior performance of the reinforcement learning agent in various environments, including temporally changing environments and real social networks, which also verifies the adaptability of our framework to different social settings.

翻译：自然界社会动物的个体如何演变以相互学习,在特定环境中学习的最佳战略是什么?在这里,我们通过采用深强化学习模式,优化多维环境中合作游戏代理方的社会学习战略(SLS),解决这两个问题。在整个培训过程中,我们发现该代理方自发地学习各种社会学习概念,如复制,侧重于经常和表现良好的邻居,自我比较,以及平衡个人和社会学习的重要性,而没有明确的指导或事先对系统的了解。完全受过训练的代理方的SLS在平均报酬方面超越了所有传统的基线SLS。我们展示了强化学习代理方在各种环境中的优异表现,包括时间变化的环境和实际的社会网络,这也证实了我们框架对不同社会环境的适应性。