# schlably: A Python Framework for Deep Reinforcement Learning Based Scheduling Experiments

arXiv: https://arxiv.org/abs/2301.04182 · HTML: https://mlsanity.com/paper/2301.04182/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 10 Jan 2023.

## Builds on (most cited first, 7 of 7)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Neural Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/1611.09940/) (2016)
- [Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning](https://mlsanity.com/paper/2010.12367/) (2020)
- [Learning to generalize Dispatching rules on the Job Shop Scheduling](https://mlsanity.com/paper/2206.04423/) (2022)
- [An actor-critic algorithm with policy gradients to solve the job shop scheduling problem using deep double recurrent agents](https://mlsanity.com/paper/2110.09076/) (2021)

## Built on by (most cited first, 3 of 3)

- [Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methods](https://mlsanity.com/paper/2308.12794/) (2023)
- [Beyond Training: Optimizing Reinforcement Learning Based Job Shop Scheduling Through Adaptive Action Sampling](https://mlsanity.com/paper/2406.07325/) (2024)
- [Decision Transformer for Enhancing Neural Local Search on the Job Shop Scheduling Problem](https://mlsanity.com/paper/2409.02697/) (2024)

## Abstract (arXiv)

Research on deep reinforcement learning (DRL) based production scheduling (PS) has gained a lot of attention in recent years, primarily due to the high demand for optimizing scheduling problems in diverse industry settings. Numerous studies are carried out and published as stand-alone experiments that often vary only slightly with respect to problem setups and solution approaches. The programmatic core of these experiments is typically very similar. Despite this fact, no standardized and resilient framework for experimentation on PS problems with DRL algorithms could be established so far. In this paper, we introduce schlably, a Python-based framework that provides researchers a comprehensive toolset to facilitate the development of PS solution strategies based on DRL. schlably eliminates the redundant overhead work that the creation of a sturdy and flexible backbone requires and increases the comparability and reusability of conducted research work.