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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.14125 |
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| _version_ | 1866916796691382272 |
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| author | Zhang, Libo Chen, Yang Takisaka, Toru Zhao, Kaiqi Li, Weidong Liu, Jiamou |
| author_facet | Zhang, Libo Chen, Yang Takisaka, Toru Zhao, Kaiqi Li, Weidong Liu, Jiamou |
| contents | Sequential Resource Allocation with situational constraints presents a significant challenge in real-world applications, where resource demands and priorities are context-dependent. This paper introduces a novel framework, SCRL, to address this problem. We formalize situational constraints as logic implications and develop a new algorithm that dynamically penalizes constraint violations. To handle situational constraints effectively, we propose a probabilistic selection mechanism to overcome limitations of traditional constraint reinforcement learning (CRL) approaches. We evaluate SCRL across two scenarios: medical resource allocation during a pandemic and pesticide distribution in agriculture. Experiments demonstrate that SCRL outperforms existing baselines in satisfying constraints while maintaining high resource efficiency, showcasing its potential for real-world, context-sensitive decision-making tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14125 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Situational-Constrained Sequential Resources Allocation via Reinforcement Learning Zhang, Libo Chen, Yang Takisaka, Toru Zhao, Kaiqi Li, Weidong Liu, Jiamou Artificial Intelligence Sequential Resource Allocation with situational constraints presents a significant challenge in real-world applications, where resource demands and priorities are context-dependent. This paper introduces a novel framework, SCRL, to address this problem. We formalize situational constraints as logic implications and develop a new algorithm that dynamically penalizes constraint violations. To handle situational constraints effectively, we propose a probabilistic selection mechanism to overcome limitations of traditional constraint reinforcement learning (CRL) approaches. We evaluate SCRL across two scenarios: medical resource allocation during a pandemic and pesticide distribution in agriculture. Experiments demonstrate that SCRL outperforms existing baselines in satisfying constraints while maintaining high resource efficiency, showcasing its potential for real-world, context-sensitive decision-making tasks. |
| title | Situational-Constrained Sequential Resources Allocation via Reinforcement Learning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2506.14125 |