pybullet implementation of icra 2022 paper on a1, Accessibility-Based Clustering for Efficient Learning of Locomotion Skills
main.py to train, test.py to test. To change the initial state distribution, change the self.configs in bittleenv.py.
Only 300 poses are used for accessibility estimation and clustering, which takes 6 hours on my laptop. Still it is effective, and I combine it with the 9 predefiend poses. To show the great efficacy, clustering 1000 poses should work. Codes are in the /k-access_preprocess folder.
Learning Curves: The proposed initial state distribution (red) outperforms the random initial states (blue).
citation:
@INPROCEEDINGS{kaccess,
author={Zhang, Chong and Yu, Wanming and Li, Zhibin},
booktitle={2022 International Conference on Robotics and Automation (ICRA)},
title={Accessibility-Based Clustering for Efficient Learning of Locomotion Skills},
year={2022},
pages={1600-1606},
doi={10.1109/ICRA46639.2022.9812113}}
SAC implementation: https://github.com/pranz24/pytorch-soft-actor-critic