Situational-Constrained Sequential Resources Allocation via Reinforcement Learning
On the map: Deep reinforcement learning methods → Deep RL algorithms and representations
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 21 of its 49 references that are in the map. First posted on arXiv 17 Jun 2025.
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21 of 49 references.
References · 21
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9 more
- 2016On Bayesian index policies for sequential resource allocationcited by 18
- 2024Off-Policy Primal-Dual Safe Reinforcement Learningcited by 14
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- 2024Probabilistic Satisfaction of Temporal Logic Constraints in Reinforcement Learning via Adaptive Policy-Switching
- 2024A Survey on Applications of Reinforcement Learning in Spatial Resource Allocation
- 2024A Comprehensive Survey on Inverse Constrained Reinforcement Learning: Definitions, Progress and Challenges
- 2025Memory Allocation in Resource-Constrained Reinforcement Learning
- 2026Reinforcement Learning in Operational Research: A Technical Review and Practical Roadmap
- 2024A Survey of Constraint Formulations in Safe Reinforcement Learning