# Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/deep-reinforcement-learning-for-machine-scheduling-methodology-the-state-of-the-2/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Maziyar Khadivi,Todd Charter,Marjan Yaghoubi,Masoud Jalayer,Maryam Ahang,Ardeshir Shojaeinasab,Homayoun Najjaran |
| Citations | 0 |
| DOI | 10.48550/arxiv.2310.03195 |
| Fields | Computer Science,Engineering |
| Open Access | true |
| OA Status | green |
| OA URL | https://arxiv.org/pdf/2310.03195 |
| OpenAlex ID | https://openalex.org/W4387427604 |
| Type | preprint |
| Year | 2023 |

## Paper authors

- [Marjan Yaghoubi](https://scholariq.org/researchers/marjan-yaghoubi/)

## Paper journal

- [arXiv (Cornell University)](https://scholariq.org/journals/arxiv-cornell-university/)

## Paper primary topic

- [Scheduling and Optimization Algorithms](https://scholariq.org/topics/scheduling-and-optimization-algorithms/)

## Paper topics

- [Scheduling and Optimization Algorithms](https://scholariq.org/topics/scheduling-and-optimization-algorithms/)
- [Metaheuristic Optimization Algorithms Research](https://scholariq.org/topics/metaheuristic-optimization-algorithms-research/)
- [Reinforcement Learning in Robotics](https://scholariq.org/topics/reinforcement-learning-in-robotics/)

---
Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.