Situational-Constrained Sequential Resources Allocation via Reinforcement Learning

Libo Zhang; Yang Chen; Toru Takisaka; Kaiqi Zhao; Weidong Li; Jiamou Liu

IJCAI 20250 citations

Situational-Constrained Sequential Resources Allocation via Reinforcement Learning

Libo Zhang, Yang Chen, Toru Takisaka, Kaiqi Zhao, Weidong Li, Jiamou Liu

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.

BibTeX

@inproceedings{ijcai2025_situationalconst,
  title = {Situational-Constrained Sequential Resources Allocation via Reinforcement Learning},
  author = {Libo Zhang and Yang Chen and Toru Takisaka and Kaiqi Zhao and Weidong Li and Jiamou Liu},
  booktitle = {IJCAI 2025},
  year = {2025}
}