Fractional Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing
Authors: Lyudong Jin, Ming Tang, Meng Zhang, Hao Wang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our proposed algorithms reduce the average Ao I by up to 57.6% compared with several non-fractional benchmarks. |
| Researcher Affiliation | Academia | Lyudong Jin*1, Ming Tang*2, Meng Zhang 1, Hao Wang3 1Zhejiang University, Haining, Zhejiang China 2Southern University of Science and Technology, Shenzhen, Guangdong China 3Monash University, Melbourne, Victoria Australia |
| Pseudocode | Yes | Algorithm 1: Fractional Q-Learning (FQL) |
| Open Source Code | No | The paper does not contain an explicit statement about releasing open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper refers to "experimental settings in (Tang and Wong 2022, Table I)" and "Appendix D" for more detailed settings, but it does not explicitly name a publicly available dataset with a direct link, DOI, or a formal citation that indicates public access. |
| Dataset Splits | No | The paper mentions evaluating performance against benchmarks but does not provide specific percentages or counts for training, validation, or test dataset splits. It refers to "experimental settings in (Tang and Wong 2022, Table I)" and "Appendix D" which are not provided in the current context. |
| Hardware Specification | No | The paper mentions performing experiments and refers to "experimental settings in (Tang and Wong 2022, Table I)" and "Appendix D" for details. However, within the provided text, there are no specific details about the hardware used (e.g., specific GPU or CPU models, memory, or cloud instances). |
| Software Dependencies | No | The paper mentions using "DDPG" and "D3QN techniques" but does not specify any version numbers for these or any other software components (e.g., programming language versions, library versions). |
| Experiment Setup | No | The paper states, "We present more detailed experiment settings in Appendix D." However, within the provided text, it does not contain specific hyperparameters, model initialization details, or training schedules. |