在分散的多代理设置中从公共制裁中获得社会规范的学习代理
A learning agent that acquires social norms from public sanctions in decentralized multi-agent settings
摘要：
社会的特点是存在各种社会规范：集体制裁模式，可以防止不协调和搭便车。受此启发，我们的目标是构建学习动力，潜在的有益的社会规范可以出现。由于社会规范的基础是制裁，我们引入了一个培训制度，代理人可以访问所有的制裁事件，但学习是分散的。这种设置在技术上是有趣的，因为制裁事件可能是分散的多代理系统中唯一可用的公共信号，其中奖励或策略共享是不可行或不可取的。为了实现集体行动，在这种情况下，我们构建了一个代理架构，包含一个分类器模块，将观察到的行为分类为批准或不批准，并根据组的动机进行惩罚。我们发现，社会规范出现在多代理系统包含这个代理和调查的条件下，这有助于他们实现社会有益的结果。
英文摘要：
Society is characterized by the presence of a variety of social norms: collective patterns of sanctioning that can prevent miscoordination and free-riding. Inspired by this, we aim to construct learning dynamics where potentially beneficial social norms can emerge. Since social norms are underpinned by sanctioning, we introduce a training regime where agents can access all sanctioning events but learning is otherwise decentralized. This setting is technologically interesting because sanctioning events may be the only available public signal in decentralized multi-agent systems where reward or policy-sharing is infeasible or undesirable. To achieve collective action in this setting we construct an agent architecture containing a classifier module that categorizes observed behaviors as approved or disapproved, and a motivation to punish in accord with the group. We show that social norms emerge in multi-agent systems containing this agent and investigate the conditions under which this helps them achieve socially beneficial outcomes.
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