---
title: "Situational-Constrained Sequential Resources Allocation via Reinforcement Learning"
authors:
  - "Libo Zhang"
  - "Yang Chen"
  - "Toru Takisaka"
  - "Kaiqi Zhao"
  - "Weidong Li"
  - "Jiamou Liu"
doi: "10.24963/ijcai.2025/1014"
url: "https://ijcai.org/proceedings/2025/1014"
pdf: "https://ijcai.org/proceedings/2025/1014.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "9121-9129"
track: "Main Track"
keywords:
  - "Uncertainty in AI: UAI: Sequential decision making"
  - "Machine Learning: ML: Reinforcement learning"
---

# Situational-Constrained Sequential Resources Allocation via Reinforcement Learning

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

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 9121-9129. DOI: [10.24963/ijcai.2025/1014](https://doi.org/10.24963/ijcai.2025/1014)

## 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.