Situational-Constrained Sequential Resources Allocation via Reinforcement Learning
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.
Cite
Text
Zhang et al. "Situational-Constrained Sequential Resources Allocation via Reinforcement Learning." International Joint Conference on Artificial Intelligence, 2025. doi:10.24963/IJCAI.2025/1014Markdown
[Zhang et al. "Situational-Constrained Sequential Resources Allocation via Reinforcement Learning." International Joint Conference on Artificial Intelligence, 2025.](https://mlanthology.org/ijcai/2025/zhang2025ijcai-situational/) doi:10.24963/IJCAI.2025/1014BibTeX
@inproceedings{zhang2025ijcai-situational,
title = {{Situational-Constrained Sequential Resources Allocation via Reinforcement Learning}},
author = {Zhang, Libo and Chen, Yang and Takisaka, Toru and Zhao, Kaiqi and Li, Weidong and Liu, Jiamou},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2025},
pages = {9121-9129},
doi = {10.24963/IJCAI.2025/1014},
url = {https://mlanthology.org/ijcai/2025/zhang2025ijcai-situational/}
}