论文标题
探索团队在多运动学习中的好处
Exploring the Benefits of Teams in Multiagent Learning
论文作者
论文摘要
对于需要合作的问题，许多多基因系统在各个代理商之间或整个人群之间实施解决方案，以实现一个共同的目标。冲突时，主要研究了多方面的团队。但是，组织心理学（OP）强调了人群中团队的好处，以学习如何协调和合作。在本文中，我们提出了一个新的多项式团队，用于增强学习（RL）代理，受人工智能团队的启发和早期工作。我们使用复杂的社会困境来验证我们的模型，这些困境在最近的多种RL中很受欢迎，并发现尽管激励不合作，但分为团队制定了合作的亲社会政策。此外，与所有代理人的利益相比，代理人能够更好地协调和学习紧急的角色，并获得更高的奖励。
For problems requiring cooperation, many multiagent systems implement solutions among either individual agents or across an entire population towards a common goal. Multiagent teams are primarily studied when in conflict; however, organizational psychology (OP) highlights the benefits of teams among human populations for learning how to coordinate and cooperate. In this paper, we propose a new model of multiagent teams for reinforcement learning (RL) agents inspired by OP and early work on teams in artificial intelligence. We validate our model using complex social dilemmas that are popular in recent multiagent RL and find that agents divided into teams develop cooperative pro-social policies despite incentives to not cooperate. Furthermore, agents are better able to coordinate and learn emergent roles within their teams and achieve higher rewards compared to when the interests of all agents are aligned.
