The code presented is used in two publications (preprint here and here).
[1] S. Gracla, E. Beck, C. Bockelmann and A. Dekorsy, "Deep Reinforcement Model Selection for Communications Resource Allocation in On-Site Medical Care", in Proc. 2022 IEEE Wireless Communications and Networking Conference (WCNC), Austin/Online, USA, 13. - 16. April 2022, pp. 1485-1490, doi: 10.1109/WCNC51071.2022.9771679.
[2] S. Gracla, E. Beck, C. Bockelmann and A. Dekorsy, "Learning Resource Scheduling with High Priority Users using Deep Deterministic Policy Gradients", in Proc. ICC 2022 - IEEE International Conference on Communications, Seoul/Online, South Korea, 16. - 20. May 2022, pp. 4480-4485, doi: 10.1109/ICC45855.2022.9838349.
Email: {gracla, beck, bockelmann, dekorsy}@ant.uni-bremen.de
The scheduling folder contains the code for [1], while scheduling_policygradient contains the code for [2].
The structure is as follows:
/
├─ [proj_name]/ | project folder
│ ├─ imports/ | contains python modules for import
│ ├─ *_config.py | contains config for this project
│ ├─ *_runner.py | orchestrates training and testing
│ ├─ *_test.py | wrappers for testing a trained scheduler
│ ├─ *_train.py | wrappers for training a scheduler
├─ .gitignore | .gitignore
├─ README.md | this file
├─ requirements.txt | python packages & versions used