# Decision Transformer for Enhancing Neural Local Search on the Job Shop Scheduling Problem

arXiv: https://arxiv.org/abs/2409.02697 · HTML: https://mlsanity.com/paper/2409.02697/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Offline RL algorithms, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 19 of its 53 references that are in the map.
- First posted on arXiv 4 Sep 2024.

## Placed in

- Offline RL and imitation learning from data / [Offline RL algorithms](https://mlsanity.com/offlinerl/offline-rl-algorithms/)

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [Learning to Perform Local Rewriting for Combinatorial Optimization](https://mlsanity.com/paper/1810.00337/) (2018)
- [Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning](https://mlsanity.com/paper/2010.12367/) (2020)
- [Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learning](https://mlsanity.com/paper/2106.01086/) (2021)
- [ChiPFormer: Transferable Chip Placement via Offline Decision Transformer](https://mlsanity.com/paper/2306.14744/) (2023)
- [Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but Improvement](https://mlsanity.com/paper/2403.15180/) (2024)
- [Self-Labeling the Job Shop Scheduling Problem](https://mlsanity.com/paper/2401.11849/) (2024)
- [A Reinforcement Learning Environment For Job-Shop Scheduling](https://mlsanity.com/paper/2104.03760/) (2021)
- [Learning to Control Local Search for Combinatorial Optimization](https://mlsanity.com/paper/2206.13181/) (2022)
- [Learning to View: Decision Transformers for Active Object Detection](https://mlsanity.com/paper/2301.09544/) (2023)
- [Causal Decision Transformer for Recommender Systems via Offline Reinforcement Learning](https://mlsanity.com/paper/2304.07920/) (2023)
- [An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint Programming](https://mlsanity.com/paper/2306.05747/) (2023)
- [schlably: A Python Framework for Deep Reinforcement Learning Based Scheduling Experiments](https://mlsanity.com/paper/2301.04182/) (2023)
- [Curriculum Learning in Job Shop Scheduling using Reinforcement Learning](https://mlsanity.com/paper/2305.10192/) (2023)
- [Beyond Training: Optimizing Reinforcement Learning Based Job Shop Scheduling Through Adaptive Action Sampling](https://mlsanity.com/paper/2406.07325/) (2024)

## Built on by (most cited first, 1 of 1)

- [Learning Memory-Enhanced Improvement Heuristics for Flexible Job Shop Scheduling](https://mlsanity.com/paper/2603.02846/) (2026)

## Abstract (arXiv)

The job shop scheduling problem (JSSP) and its solution algorithms have been of enduring interest in both academia and industry for decades. In recent years, machine learning (ML) is playing an increasingly important role in advancing existing and building new heuristic solutions for the JSSP, aiming to find better solutions in shorter computation times. In this paper we build on top of a state-of-the-art deep reinforcement learning (DRL) agent, called Neural Local Search (NLS), which can efficiently and effectively control a large local neighborhood search on the JSSP. In particular, we develop a method for training the decision transformer (DT) algorithm on search trajectories taken by a trained NLS agent to further improve upon the learned decision-making sequences. Our experiments show that the DT successfully learns local search strategies that are different and, in many cases, more effective than those of the NLS agent itself. In terms of the tradeoff between solution quality and acceptable computational time needed for the search, the DT is particularly superior in application scenarios where longer computational times are acceptable. In this case, it makes up for the longer inference times required per search step, which are caused by the larger neural network architecture, through better quality decisions per step. Thereby, the DT achieves state-of-the-art results for solving the JSSP with ML-enhanced search.