论文标题
一个合作的强化学习环境，用于检测和惩罚背叛
A Cooperative Reinforcement Learning Environment for Detecting and Penalizing Betrayal
论文作者
论文摘要
在本文中，我们提出了一个强化学习环境，该环境利用代理合作与交流，旨在探索，学习并最终惩罚在自私者行为中出现的背叛模式。我们提供了对游戏规则的描述，以及有趣的背叛和权衡的案例。初步实验研究说明了a）背叛出现，b）欺骗性剂的表现优于诚实基准，b）基于行为特征分类的背叛检测，这超过了概率检测基准。最后，我们提出了惩罚背叛的方法，列出了未来工作的方向，并提出了有趣的环境扩展，以捕获和探索越来越复杂的社交互动模式。
In this paper we present a Reinforcement Learning environment that leverages agent cooperation and communication, aimed at detection, learning and ultimately penalizing betrayal patterns that emerge in the behavior of self-interested agents. We provide a description of game rules, along with interesting cases of betrayal and trade-offs that arise. Preliminary experimental investigations illustrate a) betrayal emergence, b) deceptive agents outperforming honest baselines and b) betrayal detection based on classification of behavioral features, which surpasses probabilistic detection baselines. Finally, we propose approaches for penalizing betrayal, list directions for future work and suggest interesting extensions of the environment towards capturing and exploring increasingly complex patterns of social interactions.
