Multi-Objective Reinforcement Learning (MORL) aims to learn a set of policies that optimize trade-offs between multiple, often conflicting objectives. MORL is computationally more complex than single-objective RL, particularly as the number of objectives increases. Additionally, when objectives involve the preferences of agents or groups, ensuring fairness is socially desirable. This paper introduces a principled algorithm that incorporates fairness into MORL while improving scalability to many-objective problems. We propose using Lorenz dominance to identify policies with equitable reward distributions and introduce {\lambda}-Lorenz dominance to enable flexible fairness preferences. We release a new, large-scale real-world transport planning environment and demonstrate that our method encourages the discovery of fair policies, showing improved scalability in two large cities (Xi'an and Amsterdam). Our methods outperform common multi-objective approaches, particularly in high-dimensional objective spaces.

本文解决了多目标强化学习中的公平性与可扩展性之间的矛盾，提出了一种基于洛伦兹支配的新算法，能够在多目标问题中灵活地考虑公平性偏好。研究结果表明，该方法在西安和阿姆斯特丹的两大城市中的交通规划环境中，显著提高了相关政策的公平性和可扩展性。

具有公平性保证的可扩展多目标强化学习：使用洛伦兹支配