# FlowRL
**Repository Path**: ByteDance/FlowRL
## Basic Information
- **Project Name**: FlowRL
- **Description**: Official implementation of "Flow Based Policy for Online Reinforcement Learning"
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-07-01
- **Last Updated**: 2025-09-13
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
👋 Hi, everyone!
We are ByteDance Seed team.
You can get to know us better through the following channels👇
# Flow-based Polciy for Online Reinforcement Learning
We are delighted to introduce FlowRL. It is a new approach for online reinforcement learning that integrates flow-based policy representation with Wasserstein-2-regularized optimization. This creates a promising framework that integrates generative policies with reinforcement learning.
## News
[2025/06/10]🔥We release the PyTorch version of the code.
## Introduction
FlowRL is an Actor-Critic framework that leverages flow-based policy representation and integrates Wasserstein-2-regularized optimization. By implicitly constraining the current policy to the optimal behavioral policy via W2 distance, FlowRL achieves superior performance on challenging benchmarks like the DM_Control (Dog domain, Humanoid domain) and Humanoid_Bench.
## Getting Started
1. **Setup Conda Environment:**
Create an environment with
```bash
conda create -n flowrl python=3.11
```
2. **Clone this Repository:**
```bash
git clone https://github.com/bytedance/FlowRL.git
cd FlowRL
```
3. **Install FlowRL Dependencies:**
```bash
pip install -r requirements.txt
```
4. **Training Examples:**
- Run a single training instance:
```bash
python3 main.py --domain dog --task run
```
- Run parallel training:
```bash
bash scripts/train_parallel.sh
```
## License
This project is licensed under the Apache License 2.0. See the LICENSE file for details.
## TODO
- [ ] Release JAX version source code
## Citation
If you find FlowRL useful for your research and applications, please consider giving us a star ⭐ or cite us using:
```bibtex
@article{lv2025flow,
title={Flow-Based Policy for Online Reinforcement Learning},
author={Lv, Lei and Li, Yunfei and Luo, Yu and Sun, Fuchun and Kong, Tao and Xu, Jiafeng and Ma, Xiao},
journal={arXiv preprint arXiv:2506.12811},
year={2025}
}
```
## About [ByteDance Seed Team](https://seed.bytedance.com/)
Founded in 2023, ByteDance Seed Team is dedicated to crafting the industry's most advanced AI foundation models. The team aspires to become a world-class research team and make significant contributions to the advancement of science and society.