1. Exploring Multi-Agent Reinforcement Learning for Unrelated Parallel Machine Scheduling
- Author
-
Zampella, Maria, Otamendi, Urtzi, Belaunzaran, Xabier, Artetxe, Arkaitz, Olaizola, Igor G., Longo, Giuseppe, and Sierra, Basilio
- Subjects
Computer Science - Artificial Intelligence ,Computer Science - Machine Learning ,Computer Science - Multiagent Systems ,Computer Science - Neural and Evolutionary Computing ,I.2.1 ,I.2.11 ,I.2.8 - Abstract
Scheduling problems pose significant challenges in resource, industry, and operational management. This paper addresses the Unrelated Parallel Machine Scheduling Problem (UPMS) with setup times and resources using a Multi-Agent Reinforcement Learning (MARL) approach. The study introduces the Reinforcement Learning environment and conducts empirical analyses, comparing MARL with Single-Agent algorithms. The experiments employ various deep neural network policies for single- and Multi-Agent approaches. Results demonstrate the efficacy of the Maskable extension of the Proximal Policy Optimization (PPO) algorithm in Single-Agent scenarios and the Multi-Agent PPO algorithm in Multi-Agent setups. While Single-Agent algorithms perform adequately in reduced scenarios, Multi-Agent approaches reveal challenges in cooperative learning but a scalable capacity. This research contributes insights into applying MARL techniques to scheduling optimization, emphasizing the need for algorithmic sophistication balanced with scalability for intelligent scheduling solutions., Comment: 11 pages, 5 figures, 4 tables, article submitted to a journal
- Published
- 2024