schlably: A Python Framework for Deep Reinforcement Learning Based Scheduling Experiments
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866909276698574848 |
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| author | de Puiseau, Constantin Waubert Peters, Jannik Dörpelkus, Christian Tercan, Hasan Meisen, Tobias |
| author_facet | de Puiseau, Constantin Waubert Peters, Jannik Dörpelkus, Christian Tercan, Hasan Meisen, Tobias |
| contents | Research on deep reinforcement learning (DRL) based production scheduling (PS) has gained a lot of attention in recent years, primarily due to the high demand for optimizing scheduling problems in diverse industry settings. Numerous studies are carried out and published as stand-alone experiments that often vary only slightly with respect to problem setups and solution approaches. The programmatic core of these experiments is typically very similar. Despite this fact, no standardized and resilient framework for experimentation on PS problems with DRL algorithms could be established so far. In this paper, we introduce schlably, a Python-based framework that provides researchers a comprehensive toolset to facilitate the development of PS solution strategies based on DRL. schlably eliminates the redundant overhead work that the creation of a sturdy and flexible backbone requires and increases the comparability and reusability of conducted research work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_04182 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | schlably: A Python Framework for Deep Reinforcement Learning Based Scheduling Experiments de Puiseau, Constantin Waubert Peters, Jannik Dörpelkus, Christian Tercan, Hasan Meisen, Tobias Machine Learning Artificial Intelligence Research on deep reinforcement learning (DRL) based production scheduling (PS) has gained a lot of attention in recent years, primarily due to the high demand for optimizing scheduling problems in diverse industry settings. Numerous studies are carried out and published as stand-alone experiments that often vary only slightly with respect to problem setups and solution approaches. The programmatic core of these experiments is typically very similar. Despite this fact, no standardized and resilient framework for experimentation on PS problems with DRL algorithms could be established so far. In this paper, we introduce schlably, a Python-based framework that provides researchers a comprehensive toolset to facilitate the development of PS solution strategies based on DRL. schlably eliminates the redundant overhead work that the creation of a sturdy and flexible backbone requires and increases the comparability and reusability of conducted research work. |
| title | schlably: A Python Framework for Deep Reinforcement Learning Based Scheduling Experiments |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2301.04182 |