Ubuntu 22.04 Preparation (cu12.1)

apt update
apt install xvfb libosmesa6-dev libgl1-mesa-glx libglfw3 libglx-mesa0 libgl1-mesa-dri

mkdir -p /usr/lib/dri
SWRAST_PATH=$(find /usr -name 'swrast_dri.so' 2>/dev/null)
ln -sf "$SWRAST_PATH" /usr/lib/dri/swrast_dri.so

Check if cuda is installed

nvcc --version

If not installed, install cuda 12.1

wget https://developer.download.nvidia.com/compute/cuda/12.1.0/local_installers/cuda_12.1.0_530.30.02_linux.run
chmod +x cuda_12.1.0_530.30.02_linux.run
./cuda_12.1.0_530.30.02_linux.run --override

Environment Setup

conda create -n safepo python=3.8
conda activate safepo

conda install -c conda-forge gcc

pip install -e ./isaac-gym/python
pip install -e ./safe-policy-optimization
pip install -e ./safety-gymnasium
pip install -e ./datagen

Example Usage

Parameter Tuning

Modify the train_scripts/marl_cfg/YOUR_ALGO/config.yaml file to tune the hyperparameters. Or, add --PARAM_NAME PARAM_VALUE to the command line arguments following the train_scripts/marl_cfg/YOUR_ALGO/config.yaml file.

Commands

Run the following command to train the agent using the specified algorithm.

cd data-generation

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib
python datagen/train_scripts/macpo.py --task FreightFrankaCloseDrawer --experiment benchmark

Sync the results to the visualization server.

rsync -avz -e "ssh -p PORT" root@TRAINER_IP:path/to/safepo/runs path/to/safepo/runs

Preview the trained agent with GUI

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib
python3 data-generation/datagen/data_collect/close_drawer_viewer.py --benchmark-dir /data-generation/runs/benchmark