# 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/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Maziyar Khadivi,Todd Charter,Marjan Yaghoubi,Masoud Jalayer,Maryam Ahang,Ardeshir Shojaeinasab,Homayoun Najjaran |
| Citations | 15 |
| DOI | 10.2139/ssrn.4319327 |
| Fields | Engineering |
| Open Access | true |
| OA Status | green |
| OA URL | https://doi.org/10.2139/ssrn.4319327 |
| OpenAlex ID | https://openalex.org/W4313680365 |
| Type | preprint |
| Year | 2023 |

## Paper authors

- [Masoud Jalayer](https://scholariq.org/researchers/masoud-jalayer/)
- [Todd Charter](https://scholariq.org/researchers/todd-charter/)
- [Marjan Yaghoubi](https://scholariq.org/researchers/marjan-yaghoubi/)
- [Ardeshir Shojaeinasab](https://scholariq.org/researchers/ardeshir-shojaeinasab/)

## Paper journal

- [SSRN Electronic Journal](https://scholariq.org/journals/ssrn-electronic-journal/)

## 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/)

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