Situational-Constrained Sequential Resources Allocation via Reinforcement Learning

TL;DR

This paper presents SCRL, a reinforcement learning framework that effectively manages situational constraints in sequential resource allocation, demonstrating superior performance in real-world scenarios like healthcare and agriculture.

Contribution

Introduces a novel SCRL framework with a probabilistic constraint handling mechanism for context-dependent resource allocation tasks.

Findings

SCRL outperforms baselines in constraint satisfaction and resource efficiency

Effective handling of situational constraints in real-world scenarios

Demonstrates applicability in healthcare and agriculture contexts

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.