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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
- create workspace
mkdir MultitaskRL
cd MultitaskRL
- setup isaac-gym
- download isaac-gym from https://developer.nvidia.com/isaac-gym
- extract isaac-gym to the workspace
- 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
- 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.