奖励维度缩减用于可扩展的多目标强化学习
Reward Dimension Reduction for Scalable Multi-Objective Reinforcement Learning
AI总结：
本文提出一种在线奖励维度缩减方法，以提升多目标强化学习的可扩展性，在包括十六个目标的环境中显著优于现有方法。
摘要：
本文提出了一种简单而有效的奖励维度缩减方法，以解决多目标强化学习算法在可扩展性方面的挑战。尽管现有大多数方法专注于优化两到四个目标，但它们扩展到具有更多目标的环境中的能力仍不确定。我们的方法采用维度缩减技术来提升多目标设置下的学习效率和策略性能。虽然大多数传统维度缩减方法是为静态数据集设计的，但我们的方法针对在线学习进行了定制，并在变换后保持帕累托最优性。我们提出了一种新的用于多目标强化学习中奖励维度缩减的训练和评估框架，并在包括一个具有十六个目标的环境在内的多种环境中证明了我们方法的优越性，显著优于现有的在线维度缩减方法。
英文摘要：
In this paper, we introduce a simple yet effective reward dimension reduction method to tackle the scalability challenges of multi-objective reinforcement learning algorithms. While most existing approaches focus on optimizing two to four objectives, their abilities to scale to environments with more objectives remain uncertain. Our method uses a dimension reduction approach to enhance learning efficiency and policy performance in multi-objective settings. While most traditional dimension reduction methods are designed for static datasets, our approach is tailored for online learning and preserves Pareto-optimality after transformation. We propose a new training and evaluation framework for reward dimension reduction in multi-objective reinforcement learning and demonstrate the superiority of our method in environments including one with sixteen objectives, significantly outperforming existing online dimension reduction methods.
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。
