# Curriculum Learning in Job Shop Scheduling using Reinforcement Learning

arXiv: https://arxiv.org/abs/2305.10192 · HTML: https://mlsanity.com/paper/2305.10192/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 9 of 9)

- [Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon](https://mlsanity.com/paper/1811.06128/) (2018)
- [Neural Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/1611.09940/) (2016)
- [Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey](https://mlsanity.com/paper/2003.04960/) (2020)
- [Agent57: Outperforming the Atari Human Benchmark](https://mlsanity.com/paper/2003.13350/) (2020)
- [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)
- [Learning to generalize Dispatching rules on the Job Shop Scheduling](https://mlsanity.com/paper/2206.04423/) (2022)
- [Hybrid intelligence for dynamic job-shop scheduling with deep reinforcement learning and attention mechanism](https://mlsanity.com/paper/2201.00548/) (2022)
- [Modelling resource allocation in uncertain system environment through deep reinforcement learning](https://mlsanity.com/paper/2106.09461/) (2021)

## Built on by (most cited first, 2 of 2)

- [Decision Transformer for Enhancing Neural Local Search on the Job Shop Scheduling Problem](https://mlsanity.com/paper/2409.02697/) (2024)
- [Curriculum Learning with GNN-based Reinforcement Learning for Job Shop Scheduling](https://mlsanity.com/paper/2609.28085/) (2026)

## Abstract (arXiv)

Solving job shop scheduling problems (JSSPs) with a fixed strategy, such as a priority dispatching rule, may yield satisfactory results for several problem instances but, nevertheless, insufficient results for others. From this single-strategy perspective finding a near optimal solution to a specific JSSP varies in difficulty even if the machine setup remains the same. A recent intensively researched and promising method to deal with difficulty variability is Deep Reinforcement Learning (DRL), which dynamically adjusts an agent's planning strategy in response to difficult instances not only during training, but also when applied to new situations. In this paper, we further improve DLR as an underlying method by actively incorporating the variability of difficulty within the same problem size into the design of the learning process. We base our approach on a state-of-the-art methodology that solves JSSP by means of DRL and graph neural network embeddings. Our work supplements the training routine of the agent by a curriculum learning strategy that ranks the problem instances shown during training by a new metric of problem instance difficulty. Our results show that certain curricula lead to significantly better performances of the DRL solutions. Agents trained on these curricula beat the top performance of those trained on randomly distributed training data, reaching 3.2% shorter average makespans.