Conservative Normalizing Flows

An official implementation for Conservative Normalizing Flows (CNF).

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How to use

Clone the repo, run docker and install requirements:

cd docker/
./docker_run.sh
docker exec -it flows bash
cd docker/
install.sh

Single model runs:

1. To pre-train single Normalizing Flow model on the offline dataset:

python configs/run_flow.py --env_name <env_name to train on>

2. Select the best checkpoint from Normalizing Flow pre-training and copy it to flow_checkpoints/ directory using environment name: flow_checkpoints/halfcheetah-medium-expert-v2.pt for example.
3. To run single RL CNF training run:

python configs/run_rl.py --env_name <env_name to train on>

Hyperparameter search

1. To pre-train NF models run

python configs/sweep_flow.py --env_name <env_name to train on> --n_epoch <number of training epochs>

This command print sweep id into the terminal. Copy and paste it into configs/run_sweep_flow.py as sweep_id parameter and run

python configs/run_sweep_flow.py

2. Select the best checkpoint from Normalizing Flow pre-training and place it to flow_checkpoints/ directory.
3. Run following command, it will print RL sweep it. Copy and paste it into configs/run_sweep_rl.py as sweep_id parameter

python configs/sweep_rl.py

4. Run

python configs/run_sweep_rl.py

Expected results

Halfcheetah environment

Citing

If you find this code useful, please cite our paper:

@article{akimov2022let,
  title={Let Offline RL Flow: Training Conservative Agents in the Latent Space of Normalizing Flows},
  author={Akimov, Dmitriy and Kurenkov, Vladislav and Nikulin, Alexander and Tarasov, Denis and Kolesnikov, Sergey},
  journal={arXiv preprint arXiv:2211.11096},
  year={2022}
}

Note

• If you have any questions, please contact us at [email protected]