Proceedings

Situational-Constrained Sequential Resources Allocation via Reinforcement Learning

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

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 9121-9129.

https://doi.org/10.24963/ijcai.2025/1014

Keywords

  • Uncertainty in AI: UAI: Sequential decision making
  • Machine Learning: ML: 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.

Libo Zhang, Yang Chen, Toru Takisaka, Kaiqi Zhao, Weidong Li, and Jiamou Liu. 2025. Situational-Constrained Sequential Resources Allocation via Reinforcement Learning. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 9121–9129. https://doi.org/10.24963/ijcai.2025/1014
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