Exploring Multi-Agent Reinforcement Learning for Unrelated Parallel Machine Scheduling

• URL: http://arxiv.org/abs/2411.07634v1
• Date: Tue, 12 Nov 2024 08:27:27 GMT
• Title: Exploring Multi-Agent Reinforcement Learning for Unrelated Parallel Machine Scheduling
• Authors: Maria Zampella, Urtzi Otamendi, Xabier Belaunzaran, Arkaitz Artetxe, Igor G. Olaizola, Giuseppe Longo, Basilio Sierra,
• Abstract summary: The study introduces the Reinforcement Learning environment and conducts empirical analyses. The experiments employ various deep neural network policies for single- and Multi-Agent approaches. While Single-Agent algorithms perform adequately in reduced scenarios, Multi-Agent approaches reveal challenges in cooperative learning but a scalable capacity.
• Score: 2.3034630097498883
• License:
• 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.

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