Symbolic Distillation for Learned TCP Congestion Control
Authors: S P Sharan, Wenqing Zheng, Kuo-Feng Hsu, Jiarong Xing, Ang Chen, Zhangyang Wang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the performance of our distilled symbolic rules on both simulation and emulation environments. |
| Researcher Affiliation | Academia | S P Sharan1, Wenqing Zheng1, Kuo-Feng Hsu2, Jiarong Xing2, Ang Chen2, Zhangyang Wang1 1University of Texas at Austin 2Rice University |
| Pseudocode | No | The paper includes Figure 4, which is a diagrammatic representation of a decision tree (symbolic policy), but it does not contain textual pseudocode or a clearly labeled algorithm block. |
| Open Source Code | Yes | Our code is available at https://github.com/VITA-Group/Symbolic PCC. |
| Open Datasets | Yes | PCC-RL [7] is an open-source RL testbed for simulation of congestion control agents based on the popular Open AI Gym [43] framework. We adopt it as our main playground. |
| Dataset Splits | Yes | The return of the saved roll-outs are clustered using K-Means Clustering, and the optimal cluster number is found to be 4 using the popular elbow curve [52] and silhouette analysis [53] methods. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for experiments (e.g., CPU/GPU models, memory, or cloud provider instances). |
| Software Dependencies | No | The paper mentions software components such as PPO, Open AI Gym, Mininet, and Pantheon, but it does not specify any version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | More hyperparameter details are in our Appendix. |