Situational-Constrained Sequential Resources Allocation via Reinforcement Learning

2025-06-17

AI Summary

This work addresses context-dependent sequential resource allocation—arising in real-world applications such as pandemic medical logistics and agricultural pesticide distribution—where resource demands and agent priorities dynamically shift with contextual conditions. To overcome the limitation of conventional constrained reinforcement learning (CRL) in modeling intricate, context-sensitive constraints, we formally encode contextual constraints as logical implication relations. We further propose a dynamic penalty mechanism and a probabilistic action-selection strategy to jointly optimize constraint satisfaction and resource efficiency. Evaluated on two real-world tasks, our approach achieves substantial improvements over state-of-the-art CRL baselines: +23.6% in constraint satisfaction rate and +18.4% in resource utilization efficiency, demonstrating superior performance in balancing feasibility and optimality under dynamic contextual constraints.

Technology Category

• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• Address sequential resource allocation with situational constraints
• Develop dynamic penalty for constraint violations in RL
• Improve efficiency in context-sensitive decision-making tasks

Innovation

• Formalizes constraints as logic implications
• Dynamic penalty for constraint violations
• Probabilistic selection mechanism for constraints