imitation: Clean Imitation Learning Implementations

• URL: http://arxiv.org/abs/2211.11972v1
• Date: Tue, 22 Nov 2022 03:11:29 GMT
• Title: imitation: Clean Imitation Learning Implementations
• Authors: Adam Gleave, Mohammad Taufeeque, Juan Rocamonde, Erik Jenner, Steven H. Wang, Sam Toyer, Maximilian Ernestus, Nora Belrose, Scott Emmons, Stuart Russell
• Abstract summary: 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.
• Score: 7.7064239657103375
• License: http://creativecommons.org/licenses/by/4.0/
• 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 at https://github.com/HumanCompatibleAI/imitation

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