[ICLR 2023] CFlowNets: Continuous Control with Generative Flow Networks

Official codebase for paper CFlowNets: Continuous Control with Generative Flow Networks.

Prerequisites

Install dependencies

• torch==1.8.1
• gym==0.18.3
• python==3.8.10
• tensorboard==2.5.0

Install mujoco 210

cd ~
wget -c https://mujoco.org/download/mujoco210-linux-x86_64.tar.gz
mkdir ~/.mujoco
cd ~/.mujoco
tar -zxvf ~/mujoco210-linux-x86_64.tar.gz mujoco210

echo "export LD_LIBRARY_PATH=\$HOME/.mujoco/mujoco210/bin:\$LD_LIBRARY_PATH" >> ~/.profile
echo "export MUJOCO_PY_MUJOCO_PATH=\"\$HOME/.mujoco/mujoco210\"" >> ~/.profile
echo "export LD_LIBRARY_PATH=\"\$LD_LIBRARY_PATH:/usr/lib/nvidia\"" >> ~/.profile
pip install -U 'mujoco-py<2.2,>=2.1'

Usage

Please follow the instructions below to replicate the results in the paper.

Train retrieval network of different environments.

# Reacher
python Retrieval_Reacher.py

Train continuous flow network of different environments.

# Reacher
python CFN_Reacher.py

Remark

To reproduce the results of Figure 3 (a)-(c) in the paper, it is necessary to set the is_max variable in the select_action function to 0 to strictly sample more diverse results based on their probabilities according to the flow network. If you want to maximize the reward and reproduce the results in Figure 3 (d)-(f) in the paper, you need to set the is_max variable to 1 in order to maximize the reward.

The way of sampling according to the flow network is diverse, and as mentioned in Remark 1 in the paper:

Remark 1. After the training process, for tasks that require a larger reward, we can sample actions with the maximum flow output in P during the test process to obtain a relatively higher reward. The output of the flow model is used is flexible, and we can adjust it for different tasks.

Citation

If you find this work useful for your research, please cite our paper:

@inproceedings{li2023,
  title={CFlowNets: Continuous Control with Generative Flow Networks},
  author={Yinchuan Li and Shuang Luo and Haozhi Wang and Jianye Hao},
  booktitle={International Conference on Learning Representations},
  year={2023}
}

Contact

Please feel free to contact me via email ([email protected]) if you are interested in my research :)