/SAIL

Code for Paper "State Alignment-based Imitation Learning". Under maintenance

Primary LanguagePythonMIT LicenseMIT

State Alignment-based Imitation Learning

We propose a state-based imitation learning method for cross-morphology imitation learning. by considering both the state visitation distribution and local transition alignment.

The code is in a pre-release stage and under maintenance.

Train

The demonstration is provided in ./expert/assets/expert_traj. They are obtained from well-trained expert algorithms (SAC or TRPO).

The cross-morphology imitator can be founded in ./envs/mujoco/assets. You can also customize your own environment for training.

The main algorithm contains two settings: original imitation learning and cross-morphology imitation learning. The only difference is the pretraining stage for the inverse dynamics model.

The format should be

python sail.py --env-name [YOUR-ENV-NAME] --expert-traj-path [PATH-TO-DEMO] --beta 0.01 --resume [if want resume] --transfer [if cross morphology]

For example, for original hopper imitation:

python sail.py --env-name Hopper-v2 --expert-traj-path ./expert/assets/expert_traj/Hopper-v2_expert_traj.p --beta 0.005

for disabled swimmer imitation

python sail.py --env-name DisableSwimmer-v0 --expert-traj-path ./expert/assets/expert_traj/Swimmer-v2_expert_traj.p --beta 0.005 --transfer

Cite Our Paper

If you find it useful, please consider to cite our paper.

@article{liu2019state,
  title={State Alignment-based Imitation Learning},
  author={Liu, Fangchen and Ling, Zhan and Mu, Tongzhou and Su, Hao},
  journal={arXiv preprint arXiv:1911.10947},
  year={2019}
}

Demonstrations

Please download here and put it to ./expert/assets/expert_traj