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Action Free Guidance

Repository of AF-Guide Guiding Online Reinforcement Learning with Action-Free Offline Pretraining.

overview

Installation

conda env create -f environment.yml
conda activate afguide

Guided SAC Training

Training Guided-SAC using pretrained AFDT.

# specify different environments by changing the config files
python scripts/train_guided_sac.py  --config configs/guided_sac/hopper_medium.yaml --device="cuda"  --ckpt_root /path/to/save/model/  --name_postfix test --seed 0

Training SAC with additional guiding rewards from pretrained AFDT.

# specify different environments by changing the config files
python scripts/train_guided_sac.py  --ablation_sac_reward_sum  --config configs/guided_sac/hopper_medium.yaml --device="cuda"  --ckpt_root /path/to/save/model/  --name_postfix test --seed 0

Training pure SAC without guidance from action-free offline dataset.

# specify different environments by changing the config files
python scripts/train_guided_sac.py  --config configs/sac/hopper_medium.yaml --device="cuda"  --ckpt_root /path/to/save/model/  --name_postfix test --seed 0

AFDT Training

Download dataset first

python scripts/download_datasets_afdt.py

Train AFDT

python scripts/train_afdt.py --env antmaze-umaze-v0  # change to the dataset you want

References

@article{zhu2023guiding,
  title={Guiding Online Reinforcement Learning with Action-Free Offline Pretraining},
  author={Zhu, Deyao and Wang, Yuhui and Schmidhuber, J{\"u}rgen and Elhoseiny, Mohamed},
  journal={arXiv preprint arXiv:2301.12876},
  year={2023}
}