Residual-MPPI implementation of the MuJoCo experiments part.
conda create -n residual-mppi python=3.9
conda activate residual-mppi
pip install setuptools==66.0.0 wheel==0.38.4
pip install -r requirements.txt
You could download and unzip the file of mujoco210 via:
cd ~
mkdir .mujoco/
wget https://github.com/google-deepmind/mujoco/releases/download/2.1.0/mujoco210-linux-x86_64.tar.gz
tar -zxvf ~/.mujoco/mujoco210-linux-x86_64.tar.gz -C ~/.mujoco
Add the corresponding lines to your .bashrc file:
echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mujoco210/bin' >> ~/.bashrc
echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/lib/nvidia' >> ~/.bashrc
The core algorithm part of our method is in the mppi.py.
We provide a script main.py for the our experiments on all the MuJoCo environment, which includes the evaluation of RL prior, IL prior, and customized policies.
You could switch to any prior policies by modifying the parameters/hyperpara.yml.