Situational-Constrained Sequential Resources Allocation via Reinforcement Learning
This work addresses context-sensitive decision-making tasks in real-world applications such as healthcare and agriculture, representing an incremental improvement over traditional constraint reinforcement learning methods.
The paper tackled the problem of sequential resource allocation with situational constraints by introducing the SCRL framework, which outperformed existing baselines in satisfying constraints while maintaining high resource efficiency in scenarios like medical resource allocation during a pandemic and pesticide distribution in agriculture.
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.