For Walker environments, MuJoCo131 is required. Simply install it the same way as MuJoCo200. To swtch between different MuJoCo versions:
export MUJOCO_PY_MJPRO_PATH=~/.mujoco/mjpro${VERSION_NUM}
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mjpro${VERSION_NUM}/bin
Example of training policies and generating trajectories on multiple tasks: For point-robot and cheetah-vel:
python policy_train.py ./configs/cpearl-sparse-point-robot.json # actually dense reward is used. To run the sparse reward version, uncomment line 205 in ./Offline-MetaRL/rlkit/envs/point_robot.py.
python policy_train.py ./configs/cheetah-vel.json
python policy_train_mt1.py ./configs/cpearl-mt1.json
For Meta-World ML1 tasks (you can modify the task in ./configs/ml1.json
):
python data_collection_ml1.py ./configs/ml1.json
Generated data will be saved in ./data/
To reproduce an Meta-World ML1 experiment, run:
run_ml1.sh
To run different tasks, modify "env_name" in ./configs/cpearl-ml1.json
as well as "datadirs" in run_ml1.sh
.
Similarly, for point-robot and cheetah-vel:
run_point.sh
run_cheetah.sh