Barrier Function Inspired Reward Shaping

IssacGym implementations of various environments with Barrier Function inspired reward shaping

Environment Setup

Download and extract the Isaac Gym preview release. Supported Python versions are 3.7 or 3.8. Next create a conda or venv virtual environment and launch it.

python3 -m venv rl-env
source rl-env/bin/activate

In the python subdirectory of the extracted folder, run:

pip install -e .

This will install the isaacgym package and all of its dependencies in the active Python environment. Now clone this repo using git.

git clone https://github.com/hskalin/multi-rl.git

Running

In the multi-rl directory run

python run.py --exp-name PPO --env-id Ant

Credits

• CleanRL
• Isaac Gym Benchmark Environments