论文标题
Credo在多种学习中的重要性
The Importance of Credo in Multiagent Learning
论文作者
论文摘要
我们为系统中的代理（即多个组（即团队）中的代理提出了一个多目标优化的模型，即信条。我们的Credo模型调节了代理如何优化其属于群体的行为。我们在挑战社会困境的背景下评估了信条。我们的结果表明，团队同事或整个系统的利益无需完全一致以实现全球有益的结果。我们确定了两种情况，没有充分的共同利益，与所有代理人的利益相比，达到高等度的平等和平均人口奖励明显更高。
We propose a model for multi-objective optimization, a credo, for agents in a system that are configured into multiple groups (i.e., teams). Our model of credo regulates how agents optimize their behavior for the groups they belong to. We evaluate credo in the context of challenging social dilemmas with reinforcement learning agents. Our results indicate that the interests of teammates, or the entire system, are not required to be fully aligned for achieving globally beneficial outcomes. We identify two scenarios without full common interest that achieve high equality and significantly higher mean population rewards compared to when the interests of all agents are aligned.
