Dynamic Resource Allocation via Relational Reinforcement Learning

Abstract

This paper introduces a novel Relational Reinforcement Learning (RRL) framework designed to address the challenges of dynamic resource allocation in complex environments. Traditional Reinforcement Learning (RL) approaches often fall short when dealing with environments that constantly evolve and demand adaptive resource management. The core innovation lies in learning relational embeddings that capture the intricate dependencies between resources. The agent learns a state representation where resources are represented as points in a relational embedding space, allowing it to optimize allocation strategies based on these relationships. The reward function is explicitly designed to encourage the flow of resources between related entities, promoting efficiency and robustness. This approach represents a shift from purely individual resource optimization towards a systemic view, ultimately leading to more effective and resilient resource allocation systems. The framework is presented with detailed mathematical formulations and is intended to serve as a foundation for future research in dynamic resource management.