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Exploring Multi-Agent Reinforcement Learning for Unrelated Parallel Machine Scheduling

arXiv 2411.07634 ↗First posted 12 Nov 2024cs.AI cs.LG cs.MAcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 57 references that are in the map. First posted on arXiv 12 Nov 2024.

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References · 7

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,367
  3. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  4. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
  5. 2020Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Taskscited by 212
  6. 2020A Closer Look at Invalid Action Masking in Policy Gradient Algorithmscited by 171
  7. 2020Off-Policy Multi-Agent Decomposed Policy Gradientscited by 98

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