Hierarchical Deep Reinforcement Learning Approach for Multi-Objective Scheduling With Varying Queue Sizes

Multi-objective task scheduling (MOTS) combines the task of scheduling with the need to optimize multiple-and possibly contradicting-constraints. A challenging extension of this problem occurs when every individual task is a multiobjective optimization problem by itself. While deep reinforcement learning (DRL) has been successfully applied to complex sequential problems, its application to the MOTS domain has been stymied by two challenges. The first challenge is the inability of the DRL algorithm to ensure that every item is processed identically regardless of its position in the queue. The second challenge is the need to manage large queues, which results in large neural architectures and long training times. In this study we present MERLIN, a robust, modular and near-optimal DRL-based approach for multi-objective task scheduling. Our approach addresses both aforementioned challenges while also being more efficient and easier to train. Extensive evaluation on multiple queue sizes show that MERLIN outperforms multiple well-known scheduling algorithms by a large margin (≥ 22%).