用强化学习优化喷漆车间换色，减少颜色切换次数。
Solving the Paint Shop Problem with Flexible Management of Multi-Lane Buffers Using Reinforcement Learning and Action Masking
- 用强化学习+动作掩码，灵活管理多车道缓冲区
- 在5-15种颜色、2-8个缓冲车道下，换色数显著降低
- 对不同缓冲规模和颜色分布都表现稳定，适合工业场景
在喷漆车间问题中，需对无序到达的多色车辆序列进行重排，以最小化颜色切换次数。制造商可使用先进先出的多车道缓冲系统实现存储与取车操作。以往研究多采用贪心等简单启发式策略，或限制操作灵活性。本文提出一种强化学习方法，针对允许任意顺序存取的灵活变体，证明贪心取车最优，并通过动作掩码引入该性质。基于170个实例（2-8车道，5-15种颜色）的评估显示，该方法相较现有方法显著减少颜色切换，且对不同缓冲规模和颜色分布具有鲁棒性。
原文摘要 · Abstract (English)
In the paint shop problem, an unordered incoming sequence of cars assigned to different colors has to be reshuffled with the objective of minimizing the number of color changes. To reshuffle the incoming sequence, manufacturers can employ a first-in-first-out multi-lane buffer system allowing store and retrieve operations. So far, prior studies primarily focused on simple decision heuristics like greedy or simplified problem variants that do not allow full flexibility when performing store and retrieve operations. In this study, we propose a reinforcement learning approach to minimize color changes for the flexible problem variant, where store and retrieve operations can be performed in an arbitrary order. After proving that greedy retrieval is optimal, we incorporate this finding into the model using action masking. Our evaluation, based on 170 problem instances with 2-8 buffer lanes and 5-15 colors, shows that our approach reduces color changes compared to existing methods by considerable margins depending on the problem size. Furthermore, we demonstrate the robustness of our approach towards different buffer sizes and imbalanced color distributions.
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