When regarding the suffering of others, we often experience personal distress and feel compelled to help\footnote{Preprint. Under review.}. Inspired by living systems, we investigate the emergence of prosocial behavior among autonomous agents that are motivated by homeostatic self-regulation. We perform multi-agent reinforcement learning, treating each agent as a vulnerable homeostat charged with maintaining its own well-being. We introduce an empathy-like mechanism to share homeostatic states between agents: an agent can either \emph{observe} their partner's internal state ({\bf cognitive empathy}) or the agent's internal state can be \emph{directly coupled} to that of their partner ({\bf affective empathy}). In three simple multi-agent environments, we show that prosocial behavior arises only under homeostatic coupling - when the distress of a partner can affect one's own well-being. Additionally, we show that empathy can be learned: agents can ``decode" their partner's external emotive states to infer the partner's internal homeostatic states. Assuming some level of physiological similarity, agents reference their own emotion-generation functions to invert the mapping from outward display to internal state. Overall, we demonstrate the emergence of prosocial behavior when homeostatic agents learn to ``read" the emotions of others and then to empathize, or feel as they feel.

翻译：当目睹他人遭受痛苦时，我们常会感到个人困扰并产生帮助的冲动\footnote{预印本。正在审稿中。}。受生命系统的启发，我们研究了由稳态自我调节驱动的自主智能体之间亲社会行为的涌现机制。我们采用多智能体强化学习方法，将每个智能体视为需要维持自身福祉的脆弱稳态调节器。我们引入了一种类共情机制来实现智能体间的稳态状态共享：智能体可以\emph{观测}伙伴的内部状态（{\bf 认知共情}），或使其内部状态与伙伴状态\emph{直接耦合}（{\bf 情感共情}）。在三个简单的多智能体环境中，我们证明亲社会行为仅在家稳态耦合条件下出现——即当伙伴的痛苦能够影响自身福祉时。此外，我们发现共情能力可以通过学习获得：智能体能够“解码”伙伴的外部情绪状态以推断其内部稳态状态。在假定存在一定生理相似性的前提下，智能体通过参照自身的情绪生成函数，逆向推演从外部表达到内部状态的映射关系。总体而言，我们展示了当稳态智能体学会“解读”他人情绪并产生共情（感同身受）时，亲社会行为的涌现过程。