Installation

common dependences

pip install torch wandb hydra-core kornia pysc2 gfootball mujoco mujoco-py \
    scikit-image scikit-learn scikit-video \
    tensorboard tensorboardX pandas seaborn matplotlib opencv-python==4.5.5.64

An alternative way is to use the provided dockerfile to build docker, and before building docker image, you need to download mujoco210-linux-x86_64.tar.gz, SC2.4.10.zip and SMAC_Maps.zip in docker folder

Multi-Agent Quadcopter Control

Folling the instrctions in https://github.com/utiasDSL/gym-pybullet-drones to setup the environment. Note that MAQC need gym==0.21.0

Multi-agent MuJoCo

Following the instructios in https://github.com/openai/mujoco-py and https://github.com/schroederdewitt/multiagent_mujoco to setup a mujoco environment. In the end, remember to set the following environment variables:

LD_LIBRARY_PATH=${HOME}/.mujoco/mujoco200/bin;
LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libGLEW.so

StarCraft II & SMAC

Run the script

bash install_sc2.sh

Or you could install them manually to other path you like, just follow here: https://github.com/oxwhirl/smac.

How to run

When your environment is ready, you could run train.py provided configs. For example:

python train.py --config-path ./configs/<algo_name>/<env_name> --config-name <scenario> 

Please check the configs folder to varify current supported envs, algos and scenarios.

If you would like to change the configs of experiments, you could modify yaml files or directly modify it in cmd. For more details, please read hydra's doc.