论文标题
通过深入强化学习，通过搜索最佳启发式方法来自发地出现社会学习
Social learning spontaneously emerges by searching optimal heuristics with deep reinforcement learning
论文作者
论文摘要
自然界中社交动物的个人如何发展彼此学习，在特定环境中这种学习的最佳策略是什么？在这里，我们通过采用深厚的加强学习模型来解决这两个问题，以在多维景观中优化合作游戏中代理商的社会学习策略（SLS）。在整个培训中，我们都发现，代理商会自发地学习社会学习的各种概念，例如复制，专注于频繁且表现良好的邻居，自我比较以及个人与社会学习之间平衡的重要性，而没有任何明确的指导或对系统的任何显式指导或先验知识。来自训练有素的代理商的SLS在平均收益方面优于所有传统的基线SLS。我们证明了在各种环境中，包括时间变化的环境和真实社交网络在内的强化学习代理的出色表现，这也验证了我们框架对不同社交环境的适应性。
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.
