ET
Lei-Kun/End-to-end-DRL-for-FJSP
This is the official code of the publised paper 'A Multi-action Deep Reinforcement Learning Framework for Flexible Job-shop Scheduling Problem'
378 81 +0/wk
GitHub
disjunctive-graph-for-fjsp fjsp graph-neural-networks reinforcement-learning
Trend
3
Star & Fork Trend (21 data points)
Stars
Forks
Multi-Source Signals
Growth Velocity
Lei-Kun/End-to-end-DRL-for-FJSP has +0 stars this period . 7-day velocity: 0.3%.
Deep analysis is being generated for this repository.
Signal-backed technical analysis will be available soon.
| Metric | End-to-end-DRL-for-FJSP | MO-Gymnasium | lagom | hootenanny |
|---|---|---|---|---|
| Stars | 378 | 378 | 378 | 378 |
| Forks | 81 | 53 | 31 | 77 |
| Weekly Growth | +0 | +0 | +0 | +0 |
| Language | Python | Python | Jupyter Notebook | JavaScript |
| Sources | 1 | 1 | 1 | 1 |
| License | MIT | MIT | MIT | GPL-3.0 |
Capability Radar vs MO-Gymnasium
End-to-end-DRL-for-FJSP
MO-Gymnasium
Maintenance Activity 0
Last code push 1109 days ago.
Community Engagement 100
Fork-to-star ratio: 21.4%. Active community forking and contributing.
Issue Burden 70
Issue data not yet available.
Growth Momentum 30
No measurable growth in the current period (first-day cold start expected).
License Clarity 95
Licensed under MIT. Permissive — safe for commercial use.
Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.