用强化学习解决2D矩形打包问题，降低维度后效果接近经典算法。
Mitigating Dimensionality in 2D Rectangle Packing Problem under Reinforcement Learning Schema
- 通过简化状态和动作空间，提升建模精度
- 在标准测试集上表现媲美MaxRect启发式方法
- 可扩展至非矩形及复杂约束场景，适合研究者参考
本文探讨将强化学习（RL）应用于二维矩形打包问题。我们提出了一种简化的状态与动作空间表示，实现了高精度建模。结合UNet架构与近端策略优化（PPO），所提模型性能与MaxRect启发式方法相当。该方法具备良好泛化潜力，可拓展至非矩形打包问题及复杂约束场景，在多个标准数据集上验证了有效性。
原文摘要 · Abstract (English)
This paper explores the application of Reinforcement Learning (RL) to the two-dimensional rectangular packing problem. We propose a reduced representation of the state and action spaces that allow us for high granularity. Leveraging UNet architecture and Proximal Policy Optimization (PPO), we achieved a model that is comparable to the MaxRect heuristic. However, our approach has great potential to be generalized to nonrectangular packing problems and complex constraints.
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