# offline-RL-congestion-control

**Repository Path**: ByteDance/offline-RL-congestion-control

## Basic Information

- **Project Name**: offline-RL-congestion-control
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-02-03
- **Last Updated**: 2026-02-03

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## NOTES
This repo is for the Ten-TMS team to participate in the RL challenge. 

emulate_data_dir is supposed to located at "./emulated_dataset"
testbed_data_dir is supposed to located at "./testbed_dataset"
We also suppose a directory called data_dir for further data split which is "./" by default

Before training, the program will split the dataset into train, validation, test data, which will be moved to folders './emulated_dataset', './train_eval_dataset', './eval_dataset', respectively. 

We pre-store the behavior of baseline model for after-train evaluation. The file is baseline_eval.pkl, which is located at './emulated_dataset'. 

Please feel free to contact us if necessary.
zhangwei.666@bytedance.com

## Quick Evaluation
create ./figs directory, then run run_ten_tms_model.py

## 1. Dependencies
pip install -r requirements.txt

## 2. Generate Test Set and Validation Set
Set the path_config.json, then run test_set_generator.py 

## 3. Generate Pretrain Model
Set the pretrain_config.json, then run pretrain/train.py

## 4. Finetune Model
Set the finetune_config.json, the run fintune/train.py