=================================================================

Copyright and License

All Code in this repository - unless otherwise stated in local license or code headers is

Copyright 2024 Max Planck Institute for Intelligent Systems

Licensed under the terms of the GNU General Public Licence (GPL) v3 or higher. See: https://www.gnu.org/licenses/gpl-3.0.en.html

installation

• create workspace

mkdir MultitaskRL
cd MultitaskRL

• setup isaac-gym

1. download isaac-gym from https://developer.nvidia.com/isaac-gym
2. extract isaac-gym to the workspace
3. create conda environment and install the dependencies

bash IsaacGym_Preview_4_Package/isaacgym/create_conda_env_rlgpu.sh 
conda activate rlgpu

• clone this repository to the workspace and install dependencies

git clone https://github.com/robot-perception-group/adaptive_agent.git
pip install -r adaptive_agent/requirements.txt

Run Agent:

• enter the RL workspace

cd adaptive_agent/

• start learning in 25 environments with agents available: SAC, COMP, RMACOMP, PID

python run.py agent=COMP wandb_log=False env=BlimpRand env.num_envs=25  env.sim.headless=False

• The experiments are stored in the sweep folder. For example, hyperparameter tuning for the composition agent

wandb sweep sweep/comp_hyper.yml

The experimental results are gathered in Wandb.