This paper adds to the efforts of evolutionary ethics to naturalize morality by providing specific insights derived from a computational ethics view. We propose a stylized model of human decision-making, which is based on Reinforcement Learning, one of the most successful paradigms in Artificial Intelligence. After the main concepts related to Reinforcement Learning have been presented, some particularly useful parallels are drawn that can illuminate evolutionary accounts of ethics. Specifically, we investigate the existence of an optimal policy (or, as we will refer to, objective ethical principles) given the conditions of an agent. In addition, we will show how this policy is learnable by means of trial and error, supporting our hypotheses on two well-known theorems in the context of Reinforcement Learning. We conclude by discussing how the proposed framework can be enlarged to study other potentially interesting areas of human behavior from a formalizable perspective.

翻译：本文通过从计算伦理学视角提供的具体见解，为进化伦理学将道德自然化的努力增添了新的内容。我们提出了一种基于强化学习（人工智能领域最成功的范式之一）的人类决策风格化模型。在介绍了与强化学习相关的主要概念之后，我们勾勒出一些特别有用的类比，这些类比可以阐明伦理学的进化论解释。具体而言，我们研究了在主体给定条件下最优策略（或如我们所说的客观伦理原则）的存在性。此外，我们将展示该策略如何通过试错法被学习，并依据强化学习领域两个著名定理支撑我们的假设。最后，我们讨论了如何扩大所提出的框架，以便从可形式化的视角研究人类行为中其他潜在有趣的领域。