{"type":"ScholarlyArticle","title":"Situational-Constrained Sequential Resources Allocation via Reinforcement Learning","authors":[{"name":"Libo Zhang","slug":"libo-zhang","given":"Libo","family":"Zhang","affiliation":""},{"name":"Yang Chen","slug":"yang-chen","given":"Yang","family":"Chen","affiliation":""},{"name":"Toru Takisaka","slug":"toru-takisaka","given":"Toru","family":"Takisaka","affiliation":""},{"name":"Kaiqi Zhao","slug":"kaiqi-zhao","given":"Kaiqi","family":"Zhao","affiliation":""},{"name":"Weidong Li","slug":"weidong-li","given":"Weidong","family":"Li","affiliation":""},{"name":"Jiamou Liu","slug":"jiamou-liu","given":"Jiamou","family":"Liu","affiliation":""}],"abstract":"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.","doi":"10.24963/ijcai.2025/1014","url":"https://ijcai.org/proceedings/2025/1014","pdf":"https://ijcai.org/proceedings/2025/1014.pdf","markdown":"https://ijcai.org/proceedings/2025/1014.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1014","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Uncertainty in AI","track":"Main Track","number":1014,"pages":{"first":9121,"last":9129},"keywords":["Uncertainty in AI: UAI: Sequential decision making","Machine Learning: ML: Reinforcement learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}