基于洛伦兹支配的可扩展多目标强化学习与公平性保证
Scalable Multi-Objective Reinforcement Learning with Fairness Guarantees using Lorenz Dominance
AI总结：
本文提出一种结合公平性保证的多目标强化学习算法，通过洛伦兹支配识别公平奖励分布，提升多目标问题的可扩展性，在西安和阿姆斯特丹两个大型城市中展示出优于传统方法的性能。
摘要：
多目标强化学习（MORL）旨在学习一组政策，以优化多个往往冲突的客观之间的权衡。与单目标强化学习相比，MORL计算复杂性更高，尤其当目标数量增加时更为明显。此外，当目标涉及代理或群体的偏好时，融入公平性变得重要且具有社会价值。本文提出一种原理性算法，将公平性融入MORL并提高其在多目标问题中的可扩展性。我们提出使用洛伦兹支配来识别具有公平奖励分布的策略，并引入lambda-洛伦兹支配以实现灵活的公平偏好。我们发布了一个新的大规模真实世界交通规划环境，并证明我们的方法促进了公平策略的发现，展示了在两个大型城市（西安和阿姆斯特丹）中改进的可扩展性。我们的方法在高维目标空间中优于传统多目标方法。
英文摘要：
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, incorporating fairness becomes both important and 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
