RIZE: Adaptive Regularization for Imitation Learning

Paper: TMLR 2025 | arXiv

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RIZE is a novel IRL method with adaptive reward bounds and distributional critics, achieving expert-level performance on MuJoCo and Adroit tasks using only 3–10 expert demonstrations. It outperforms IQ-Learn, LSIQ, SQIL, CSIL, and BC—especially on high-DoF tasks like Humanoid-v2 and Hammer-v1. See the project page for results, and visuals.

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Installation

1. Clone the repository

   git clone https://github.com/adibka/RIZE.git
   cd RIZE

2. Create an environment (Python 3.10)

   python3 -m venv venvs/rize
   source venvs/rize/bin/activate

3. Install mujoco-py

   Note: mujoco-py==2.0.2.0 requires MuJoCo version 2.0 binaries. Follow the guide at mujoco-py docs if needed.

   mkdir -p ~/.mujoco && cd ~/.mujoco
   curl -O https://www.roboti.us/download/mujoco200_linux.zip
   curl -O https://www.roboti.us/file/mjkey.txt
   unzip mujoco200_linux.zip
   mv mujoco200_linux mujoco200
   rm mujoco200_linux.zip

   Add to ~/.bashrc (replace {user-name} with your actual username):

   echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/{user-name}/.mujoco/mujoco200/bin' >> ~/.bashrc
   source ~/.bashrc

4. Install dependencies

   pip install -r requirements.txt

   Note: requirements.txt pins tested versions (including mujoco-py, gym, gymnasium, mujoco). Verified on Ubuntu 22.04 + Python 3.10.

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Expert Demonstrations

Download pre-generated expert trajectories:

cd RIZE
./download_experts.sh

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Training

Run training with a single command. Hyperparameters are detailed in the paper appendix (Table 1).

python main.py --env halfcheetah --demos 10 --seed 0

Replace --env with any supported task:
halfcheetah, walker2d, ant, humanoid, hopper, hammer

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Citation

@article{karimi2025rize,
  title={RIZE: Adaptive Regularization for Imitation Learning},
  author={Karimi, Adib and Ebadzadeh, Mohammad Mehdi},
  journal={Transactions on Machine Learning Research},
  year={2025},
  url={https://openreview.net/forum?id=a6DWqXJZCZ}
}

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