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

Waldemar Kołodziejczyk, Mariusz Kaleta
9/15/2024
cs.LGmath.OC

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Abstract

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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Cite this paper

@article{koodziejczyk2024mitigating,
  title  = {Mitigating Dimensionality in 2D Rectangle Packing Problem under Reinforcement Learning Schema},
  author = {Waldemar Kołodziejczyk and Mariusz Kaleta},
  year   = {2024},
  eprint = {2409.09677},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2409.09677v1}
}

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