Mitigating Dimensionality in 2D Rectangle Packing Problem under Reinforcement Learning Schema
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866929511891730432 |
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| author | Kołodziejczyk, Waldemar Kaleta, Mariusz |
| author_facet | Kołodziejczyk, Waldemar Kaleta, Mariusz |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_09677 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mitigating Dimensionality in 2D Rectangle Packing Problem under Reinforcement Learning Schema Kołodziejczyk, Waldemar Kaleta, Mariusz Machine Learning Optimization and Control 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. |
| title | Mitigating Dimensionality in 2D Rectangle Packing Problem under Reinforcement Learning Schema |
| topic | Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2409.09677 |