Summary of Decision Transformer For Enhancing Neural Local Search on the Job Shop Scheduling Problem, by Constantin Waubert De Puiseau et al.
Decision Transformer for Enhancing Neural Local Search on the Job Shop Scheduling Problem
by Constantin Waubert de Puiseau, Fabian Wolz, Merlin Montag, Jannik Peters, Hasan Tercan, Tobias Meisen
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper builds upon a state-of-the-art deep reinforcement learning (DRL) agent called Neural Local Search (NLS), which efficiently controls local neighborhood searches on the Job Shop Scheduling Problem (JSSP). A method is developed to train the Decision Transformer (DT) algorithm on search trajectories taken by the trained NLS agent, aiming to improve learned decision-making sequences. The DT successfully learns local search strategies that are different and often more effective than those of the NLS agent itself. In scenarios where longer computational times are acceptable, the DT achieves state-of-the-art results for solving the JSSP with ML-enhanced searches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses machine learning to help solve a long-standing problem called Job Shop Scheduling. It takes an existing algorithm and makes it better by using something called Decision Transformer. This helps find even better solutions in less time. The research shows that this new method is really good at solving the problem when you have more time to spare. |
Keywords
» Artificial intelligence » Machine learning » Reinforcement learning » Transformer
