This is the code repository for the paper "Gradient Informed Proximal Policy Optimization", which was presented in the Neurips 2023 conference. This code was implemented on the basis of rl_games and SHAC.
We need following packages.
- pytorch 1.13.1 (https://pytorch.org/get-started/previous-versions/)
- pyyaml 6.0.1 (pip install pyyaml)
- tensorboard (pip install tensorboard)
- tensorboardx 2.6.2 (pip install tensorboardx)
- urdfpy (pip install urdfpy)
- usd-core 23.8 (pip install usd-core)
- ray 2.6.2 (pip install ray)
- ninja 1.10.2 (conda install -c conda-forge ninja)
- cudatoolkit (conda install -c anaconda cudatoolkit)
- cudatoolkit-dev (conda install -c conda-forge cudatoolkit-dev)
- optuna 3.2.0 (pip install optuna)
- optuna-dashboard 0.11.0 (pip install optuna-dashboard)
- matplotlib (pip install matplotlib)
- highway-env 1.8.2 (pip install highway-env)
- seaborn (pip install seaborn)
- gym (pip install gym)
Then, run following command.
pip install -e .
Run following command for function optimization problems.
bash ./run_func_optim.sh