Mitigating Dimensionality in 2D Rectangle Packing Problem under Reinforcement Learning Schema
<|reference_start|>Mitigating Dimensionality in 2D Rectangle Packing Problem under Reinforcement Learning Schema: 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.<|reference_end|>
@article{kołodziejczyk2024mitigating,
    title={Mitigating Dimensionality in 2D Rectangle Packing Problem under
  Reinforcement Learning Schema},
    author={Waldemar Ko{l}odziejczyk, Mariusz Kaleta},
    journal={Progress in Polish Artificial Intelligence Research, 4(5), pp.
  412-418, 2024},
    year={2024},
    doi={10.17388/WUT.2024.0002.MiNI},
    archivePrefix={arXiv},
    eprint={2409.09677},
    primaryClass={cs.LG math.OC}
}