Situational-Constrained Sequential Resources Allocation via Reinforcement Learning

• URL: http://arxiv.org/abs/2506.14125v1
• Date: Tue, 17 Jun 2025 02:40:49 GMT
• Title: Situational-Constrained Sequential Resources Allocation via Reinforcement Learning
• Authors: Libo Zhang, Yang Chen, Toru Takisaka, Kaiqi Zhao, Weidong Li, Jiamou Liu,
• Abstract summary: Sequential Resource Allocation with situational constraints presents a significant challenge in real-world applications.<n>This paper introduces a novel framework, SCRL, to address this problem.<n>We develop a new algorithm that dynamically penalizes constraint violations.
• Score: 17.8234166913582
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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