标题： 学习多智能体系统中的公平性 显示英文标题

标题： Learning Fairness in Multi-Agent Systems

摘要： 公平性对人类社会至关重要，有助于稳定性和生产力。 同样，公平性也是许多多智能体系统的关键。 将公平性引入多智能体学习可以帮助多智能体系统变得高效且稳定。 然而，同时实现学习效率和公平性是一个复杂、多目标的联合策略优化问题。 为了解决这些困难，我们提出了FEN，一种新颖的分层强化学习模型。 我们首先为每个智能体分解公平性，并提出公平高效的奖励，使每个智能体学习其自身的策略以进行优化。 为了避免多目标冲突，我们设计了一个由控制器和多个子策略组成的层次结构，其中控制器通过在提供多样化行为以与环境交互的子策略之间切换，来最大化公平高效的奖励。 FEN可以以完全去中心化的方式进行训练，使其易于部署在现实世界的应用中。 实证上，我们表明FEN能够轻松学习公平性和效率，并在多种多智能体场景中显著优于基线方法。 显示英文摘要

摘要： Fairness is essential for human society, contributing to stability and productivity. Similarly, fairness is also the key for many multi-agent systems. Taking fairness into multi-agent learning could help multi-agent systems become both efficient and stable. However, learning efficiency and fairness simultaneously is a complex, multi-objective, joint-policy optimization. To tackle these difficulties, we propose FEN, a novel hierarchical reinforcement learning model. We first decompose fairness for each agent and propose fair-efficient reward that each agent learns its own policy to optimize. To avoid multi-objective conflict, we design a hierarchy consisting of a controller and several sub-policies, where the controller maximizes the fair-efficient reward by switching among the sub-policies that provides diverse behaviors to interact with the environment. FEN can be trained in a fully decentralized way, making it easy to be deployed in real-world applications. Empirically, we show that FEN easily learns both fairness and efficiency and significantly outperforms baselines in a variety of multi-agent scenarios.