提出一种新方法，让多目标强化学习能高效处理超多目标场景。
Reward Dimension Reduction for Scalable Multi-Objective Reinforcement Learning
- 用动态维度压缩技术，在线学习中保留最优解集
- 在16个目标环境中表现远超现有方法
- 适合需要扩展多目标能力的研究者
本文提出一种简单有效的奖励维度缩减方法，解决多目标强化学习算法的可扩展性挑战。现有方法大多仅支持2到4个目标，难以扩展至更多目标。本文方法针对在线学习设计，虽基于传统降维思想，但专为动态环境优化，确保变换后仍保持帕累托最优性。我们构建了新的训练与评估框架，实证表明该方法在包含16个目标的环境中显著优于现有在线降维方法，大幅提升学习效率与策略性能。
原文摘要 · Abstract (English)
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.
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