imitation: Clean Imitation Learning Implementations
@misc{gleave2022imitationcleanimitationlearning, title={imitation: Clean Imitation Learning Implementations}, author={Adam Gleave and Mohammad Taufeeque and Juan Rocamonde and Erik Jenner and Steven H. Wang and Sam Toyer and Maximilian Ernestus and Nora Belrose and Scott Emmons and Stuart Russell}, year={2022}, eprint={2211.11972}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2211.11972}, }
September 21, 2022
Abstract
imitation provides open-source implementations of imitation and reward learning algorithms in PyTorch. We include three inverse reinforcement learning (IRL) algorithms, three imitation learning algorithms and a preference comparison algorithm. The implementations have been benchmarked against previous results, and automated tests cover 98% of the code. Moreover, the algorithms are implemented in a modular fashion, making it simple to develop novel algorithms in the framework. Our source code, including documentation and examples, is available [here](https://github.com/HumanCompatibleAI/imitation). <>
