Awesome

Model-based Offline Policy Optimization (MOPO)

Code to reproduce the experiments in MOPO: Model-based Offline Policy Optimization.

Installation

1. Install MuJoCo 2.0 at ~/.mujoco/mujoco200 and copy your license key to ~/.mujoco/mjkey.txt
2. Create a conda environment and install mopo

cd mopo
conda env create -f environment/gpu-env.yml
conda activate mopo
# Install viskit
git clone https://github.com/vitchyr/viskit.git
pip install -e viskit
pip install -e .

Usage

Configuration files can be found in examples/config/. For example, run the following command to run HalfCheetah-mixed benchmark in D4RL.

mopo run_local examples.development --config=examples.config.d4rl.halfcheetah_mixed --gpus=1 --trial-gpus=1

Currently only running locally is supported.

Logging

This codebase contains viskit as a submodule. You can view saved runs with:

viskit ~/ray_mopo --port 6008

assuming you used the default log_dir.

Citing MOPO

If you use MOPO for academic research, please kindly cite our paper the using following BibTeX entry.

@article{yu2020mopo,
  title={MOPO: Model-based Offline Policy Optimization},
  author={Yu, Tianhe and Thomas, Garrett and Yu, Lantao and Ermon, Stefano and Zou, James and Levine, Sergey and Finn, Chelsea and Ma, Tengyu},
  journal={arXiv preprint arXiv:2005.13239},
  year={2020}
}