Situational-Constrained Sequential Resources Allocation via Reinforcement Learning

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.

@article{zhang2025_2506.14125,
  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 },
  journal={arXiv preprint arXiv:2506.14125},
  year={ 2025 }
}