https://github.com/bytedance/flowrl
Official implementation of "Flow Based Policy for Online Reinforcement Learning"
Science Score: 36.0%
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○CITATION.cff file
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○DOI references
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✓Academic publication links
Links to: arxiv.org -
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○Scientific vocabulary similarity
Low similarity (10.7%) to scientific vocabulary
Keywords
Repository
Official implementation of "Flow Based Policy for Online Reinforcement Learning"
Basic Info
- Host: GitHub
- Owner: bytedance
- License: apache-2.0
- Language: Python
- Default Branch: main
- Homepage: https://github.com/bytedance/FlowRL
- Size: 81.1 KB
Statistics
- Stars: 20
- Watchers: 0
- Forks: 0
- Open Issues: 1
- Releases: 0
Topics
Metadata Files
README.md
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 DMControl (Dog domain, Humanoid domain) and HumanoidBench.
Getting Started
Setup Conda Environment: Create an environment with
bash conda create -n flowrl python=3.11Clone this Repository:
bash git clone https://github.com/bytedance/FlowRL.git cd FlowRLInstall FlowRL Dependencies:
bash pip install -r requirements.txtTraining Examples:
- Run a single training instance:
bash python3 main.py --domain dog --task run
- Run a single training instance:
- 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
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.
Owner
- Name: Bytedance Inc.
- Login: bytedance
- Kind: organization
- Location: Singapore
- Website: https://opensource.bytedance.com
- Twitter: ByteDanceOSS
- Repositories: 255
- Profile: https://github.com/bytedance
GitHub Events
Total
- Issues event: 1
- Watch event: 16
- Push event: 1
- Public event: 1
Last Year
- Issues event: 1
- Watch event: 16
- Push event: 1
- Public event: 1
Dependencies
- dm_control *
- gymnasium *
- imageio *
- mujoco *
- tensorboard *
- torch *
- torchvision *