In the enhanced GUARD environment, RL training benefits from the power of GPU parallelization, enabling the training of RL agents in a matter of minutes.
clone this repo
conda create -n guardX
pip install -r requirements.txt
pip install -U "jax[cuda11_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
cd safe_rl_envs pip install -e.
cd a path for Issac Gym(can be different from the path of guardX)
Download and install Isaac Gym Preview 4 from https://developer.nvidia.com/isaac-gym
cd isaacgym/python && pip install -e .
Try running an example cd examples && python3 1080_balls_of_solitude.py
cd back to the guardX repo cd IsaacGymEnvs pip install -e.
cd safe_rl_libX/trpo python3 trpo.py --task AllegroKukaTwoArmsLSTM --env_num 2000