WiseRL provides banchmarked PyTorch implementations for Offline Preference-Based RL algorithms, including:

• Oracle-IQL & Oracle AWAC
• Supervised Finetuning
• Bradley-Terry Model + IQL/AWAC
• Contrastive Prefereing Learning
• Inverse Preference Learning + IQL/AWAC
• Preference Transformer + IQL/AWAC
• Hindsight Preference Learning + IQL/AWAC

# for reward-model free algorithms
python3 scripts/main.py --config /path/to/config.yaml

# for reward-model-based algorithms
python3 scripts/rmb_main.py --config /path/to/config.yaml

• clone this repo and install the dependencies

  git clone [email protected]:typoverflow/WiseRL
  cd WiseRL && pip install -e .

• install environment or dataset dependencies
  • for D4RL experiments:

    git clone https://github.com/Farama-Foundation/d4rl.git
    cd d4rl
    pip install -e .

  • for metaworld experiments:

    git clone [email protected]:Farama-Foundation/Metaworld
    cd Metaworld && git checkout 04be337a
    pip install -e .

  • for robosuite experiments (we follow the instructions from IPL):
    • Git clone the robosuite repository, checkout to offline_study branch and install.

      git clone https://github.com/ARISE-Initiative/robosuite
      cd robosuite && git checkout offline_study
      pip install -e . --no-dependencies

      Nota that if you are using python 3.10 or higher, you need to change from collections import Iterable to from collections.abc import Iterable in file robosuite/models/arenas/multi_table_arena.py.
    • Run import robosuite repeatedly until it completes. Install the missing packages if any error shows up.
    • Git clone the robomimic repository.

      git clone [email protected]:ARISE-Initiative/robomimic.git
      cd robomimic && git checkout v0.2.0

    • Download the robomimic dataset

      mkdir -p ~/.robomimic/datasets/
      cd robomimic/scripts/
      python download_datasets.py --tasks sim --dataset_types ph --hdf5_types low_dim --download_dir ~/.robomimic/datasets/
      python download_datasets.py --tasks sim --dataset_types mh --hdf5_types low_dim --download_dir ~/.robomimic/datasets/

    • Checkout back to the master branch and install. Note that you must first checkout to v0.2.0 branch to download the dataset, and come back to install the latest version of code.

      git checkout master
      pip install -e . --no-dependencies

    • Run import robomimic repeatedly until it completes. Install the missing packages if any error shows up.
    • Note that the above installation scripts will download the datasets to ~/.robosuite/datasets. If you would like to change to other locations, please make sure to change the macro in Robomimic Dataset accordingly.

• Research Lightening and Inverse Preference Learning: for their module design and implementations of some baseline algorithms.
• Preference Transformer: for the Gym-MuJoCo datasets.

@software{wiserl
  title = {{WiseRL: Benchmarked Implementations of Offline Preference-based RL Algorithms}},
  author = {Gao, Chen-Xiao and Shengjun, Fang},
  month = feb,
  url = {https://github.com/typoverflow/WiseRL},
  year = {2024}
}

@article{gao2024hindsight,
  title={Hindsight Preference Learning for Offline Preference-based Reinforcement Learning},
  author={Chen-Xiao Gao and Shengjun Fang and Chenjun Xiao and Yang Yu and Zongzhang Zhang},
  journal={arXiv preprint arXiv:2407.04451},
  year={2024},
}