Mitigating Dimensionality in 2D Rectangle Packing Problem under Reinforcement Learning Schema

2024-09-15 · Waldemar Kołodziejczyk, Mariusz Kaleta

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.

📄 PDF Abstract BibTeX arXiv:2409.09677

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reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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